Mutational bias and emergence of drug resistance in the human immunodeficiency virus type 1
Bibliographic record
Abstract
E138K, a G→A mutation in the human immunodeficiency virus type 1 (HIV-1) reverse transcriptase (RT), is preferentially selected by etravirine (ETR) and rilpivirine (RPV) over other substitutions at position E138 that offer greater drug resistance. We hypothesized that there was a mutational bias for the E138K substitution and designed an allele-specific PCR to monitor the emergence of E138A/G/K/Q/R/V during ETR or RPV selection experiments. E138K, as well as E138G, consistently emerged first during selection experiments, followed by E138A, E138Q and E138R. Surprisingly, E138K was identified as a minority in 23% of drug-naïve subtype B patients, and was not further enriched in patients with the M184I substitution. The high prevalence of E138K minority species could reflect a low fitness cost of E138K; however, E138K was one of the least fit substitutions at codon E138, even after taking into account the dNTP pools of the cells used in competition experiments. Ultra-deep sequencing analysis revealed other minority species in a pattern consistent with the mutational bias of HIV-1 RT. These results confirm the mutational bias of HIV-1 in patients and highlight the importance of G→A mutations in HIV-1 drug resistance evolution.This G→A bias reflects enriched adenosine in HIV-1 codons, a feature that is mysteriously targeted by the anti-HIV-1 restriction factor, Schlafen family protein 11 (SLFN11). Our in silico modeling of SLFN11 suggested putative structure-function relationships and a relation to Ski2-family RNA helicases.
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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".