Evolution of protease inhibitor resistance in the gag and pol genes of HIV subtype G isolates
Bibliographic record
Abstract
OBJECTIVES: To analyse HIV Gag cleavage site (CS) and non-CS mutations in HIV non-B isolates from patients failing antiretroviral therapy. PATIENTS AND METHODS: Twenty-one HIV isolates were obtained from patients infected with HIV subtype G during an outbreak in Russia 20 years ago. Most patients were failing antiretroviral therapy when genotyping was performed. RESULTS: HIV Gag CS mutations accumulated in protease inhibitor (PI)-resistant HIV isolates and were correlated with the presence of three or more PI resistance mutations. Only 1 of 11 HIV isolates carrying major protease mutations did not harbour treatment-associated CS mutations. Natural polymorphism 453T, often found in HIV non-B subtypes, seems to favour the selection of CS mutation 453I rather than treatment-associated CS mutation 453L. Resistance-associated non-CS mutations (123E and 200I) were also observed in PI-resistant clinical isolates. Non-CS mutations in the frameshift-regulating site, which controls the synthesis of Gag-Pol, did not affect frameshift efficiency in dual luciferase assays. Of note, one of four HIV isolates from patients failing PI therapies without protease mutations harboured Gag mutations associated with PI resistance (123E and 436R) and reverse transcriptase inhibitor mutations conferring resistance to the backbone drug. CONCLUSIONS: HIV Gag CS mutations commonly occurred in HIV isolates from patients failing PI therapies and natural polymorphisms at the same position influence their emergence. Non-CS mutations previously associated with PI resistance were also observed in clinical isolates. Gag mutations might indicate the evolution of PI resistance even in the absence of protease mutations.
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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 | 0.001 |
| 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 | 0.000 |
| 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".