Rare mutations at codon 103 of HIV-1 reverse transcriptase can confer resistance to non-nucleoside reverse transcriptase inhibitors
Bibliographic record
Abstract
BACKGROUND: The K103N mutation in HIV-1 reverse transcriptase (RT) confers high-level resistance to current non-nucleoside reverse transcriptase inhibitors (NNRTI). The prevalence and resistance profile of HIV-1 with other substitutions at RT codon 103 is less well documented. METHODS: K103 substitutions among over 70,000 clinical samples submitted for routine antiretroviral resistance testing at two independent centres were examined. Phenotypic resistance profiles of isolates harboring rare K103 variants in the absence of known NNRTI-associated resistance mutations were retrieved from Virco's correlative genotype/phenotype database. Genotyped samples with known treatment histories were retrieved from the British Columbia Centre for Excellence in HIV/AIDS database. Site-directed mutants containing K103 variants were constructed and phenotyped. RESULTS: K103N, R and S were observed in 29, 1.8, and 0.9% of Virco isolates and in 16, 1.5 and 0.4% of British Columbia isolates. K103T/Q/H substitutions were observed only rarely (<0.2%). The prevalence of unusual codon 103 substitutions remained stable over 5 years, except K103S, which increased over fourfold in both datasets. K103R/Q-containing clinical isolates remained phenotypically susceptible to NNRTI, whereas K103S/T/H-containing isolates showed over 10-fold decreased NNRTI susceptibility. Among patients with a known treatment history, K103S/T/H were observed primarily in individuals failing NNRTI-containing regimens. Site-directed mutants confirmed decreased susceptibility to NNRTI in K103S/T/H-containing recombinants. CONCLUSION: Variants at HIV RT codon 103 other than K103N are observed relatively rarely in clinical isolates, but K103 S, T and H confer decreased susceptibility to NNRTI. These data are relevant for interpretive genotype algorithms and in the design of assays specific to RT codon 103 mutations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 teacher head, 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".