Silent mutations are selected in HIV-1 reverse transcriptase and affect enzymatic efficiency
Bibliographic record
Abstract
OBJECTIVE: Missense mutations in HIV-1 reverse transcriptase are frequently selected in response to therapy; we examined whether silent mutations were also selected for by HIV therapy. DESIGN: Retrospective, observational analysis. Biochemical assays. METHODS: A comparison of the reverse transcriptase gene, from antiretroviral- naive (N = 812) and experienced individuals (N = 2212), reveals two silent mutations (K65K and K66K) that are strongly associated with treatment experience. To assess reverse transcription efficiency, steady-state kinetic assays were carried out using recombinant purified HIV-1 reverse transcriptase and a series of synthetic RNA/DNA template/primer substrates. The RNA templates spanned codons 60-77 in the reverse transcriptase and included different combinations of mutations at codons 65, 66, 67, and 70. RESULTS: Silent AAG mutations (or mixtures) at reverse transcriptase codons 65 and/or 66 were observed in 812 samples from 351 patients and 2129 samples from 829 patients, respectively. In clade B samples, there was a very strong relationship between the silent mutations and the thymidine analogue mutations, in particular D67N. Steady-state kinetic experiments demonstrated that HIV-1 reverse transcriptase exhibited a strong tendency to pause and/or dissociate at codons 65 and 66 on RNA templates that contained the D67N and K70R mutations. However, when the K66 or K66 AAA to AAG mutations were added to the background of the 67 and 70 mutational changes, these pausing and/or dissociation events were largely alleviated. CONCLUSION: Silent mutations at codons 65 and/or 66 are strongly coselected with thymidine analogue mutations. These data provide the first evidence for an RNA-level mechanism of direct relevance to drug resistance.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".