Targeted editing of the PSIP1 gene encoding LEDGF/p75 protects cells against HIV infection
Bibliographic record
Abstract
To fulfill a productive infection cycle the human immunodeficiency virus (HIV) relies on host-cell factors. Interference with these co-factors holds great promise in protecting cells against HIV infection. LEDGF/p75, encoded by the PSIP1 gene, is used by the integrase (IN) protein in the pre-integration complex of HIV to bind host-cell chromatin facilitating proviral integration. LEDGF/p75 depletion results in defective HIV replication. However, as part of its cellular function LEDGF/p75 tethers cellular proteins to the host-cell genome. We used site-specific editing of the PSIP1 locus using CRISPR/Cas to target the aspartic acid residue in position 366 and mutated it to asparagine (D366N) to disrupt the interaction with HIV IN but retain LEDGF/p75 cellular function. The resulting cell lines demonstrated successful disruption of the LEDGF/p75 HIV-IN interface without affecting interaction with cellular binding partners. In line with LEDGF/p75 depleted cells, D366N cells did not support HIV replication, in part due to decreased integration efficiency. In addition, we confirm the remaining integrated provirus is more silent. Taken together, these results support the potential of site-directed CRISPR/Cas9 mediated knock-in to render cells more resistant to HIV infection and provides an additional strategy to protect patient-derived T-cells against HIV-1 infection as part of cell-based therapy.
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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 | 0.000 | 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) | 0.001 | 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".