Silent mutations at codons 65 and 66 in reverse transcriptase alleviate indel formation and restore fitness in subtype B HIV-1 containing D67N and K70R drug resistance mutations
Bibliographic record
Abstract
Resistance to combined antiretroviral therapy (cART) in HIV-1-infected individuals is typically due to nonsynonymous mutations that change the protein sequence; however, the selection of synonymous or 'silent' mutations in the HIV-1 genome with cART has been reported. These silent K65K and K66K mutations in the HIV-1 reverse transcriptase (RT) occur in over 35% of drug-experienced individuals and are highly associated with the thymidine analog mutations D67N and K70R, which confer decreased susceptibility to most nucleoside and nucleotide RT inhibitors. However, the basis for selection of these silent mutations under selective drug pressure is unknown. Using Illumina next-generation sequencing, we demonstrate that the D67N/K70R substitutions in HIV-1 RT increase indel frequency by 100-fold at RT codons 65-67, consequently impairing viral fitness. Introduction of either K65K or K66K into HIV-1 containing D67N/K70R reversed the error-prone DNA synthesis at codons 65-67 in RT and improved viral replication fitness, but did not impact RT inhibitor drug susceptibility. These data provide new mechanistic insights into the role of silent mutations selected during antiretroviral therapy and have broader implications for the relevance of silent mutations in the evolution and fitness of RNA viruses.
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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 | 0.000 |
| 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".