Defective HIV-1 quasispecies in the form of multiply drug-resistant proviral DNA within cells can be rescued by superinfection with different subtype variants of HIV-1 and by HIV-2 and SIV
Bibliographic record
Abstract
OBJECTIVES: HIV-1 generates swarms of similar, but genetically distinct, variants termed quasispecies and many of these variants can be defective. A relevant question is whether such defective species can contribute to viral pathogenesis. Indeed, we previously reported that a presumed recombination of defective proviral DNA with other complementary defective proviral DNA or with wild-type viral DNA in the aftermath of superinfection could lead to the rescue of defective provirus and the production of replication-competent virus. We then wished to determine whether such rescue could be affected by viruses of different subtypes or even by other members of the retrovirus family. METHODS: Here, we have used drug resistance mutations within the HIV genome as markers of potential recombination. RESULTS: We show that a defective proviral DNA within cells can be rescued by the superinfection of MT2 cells by various subtypes of HIV-1, and by HIV-2 and simian immunodeficiency virus, but not by human T cell leukaemia virus type 1 or by human herpes virus-6. The drug-resistance phenotype of the rescued HIV was confirmed in a second round of infection. CONCLUSIONS: Defective proviral HIV-1 can be rescued by the infection by different variants of HIV-1 and by several other retroviruses as well.
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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) | 0.002 | 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".