Does antiretroviral treatment change HIV-1 codon usage patterns in its genes: a preliminary bioinformatics study
Bibliographic record
Abstract
BACKGROUND: Codon usage bias has been described for various organisms and is thought to contribute to the regulation of numerous biological processes including viral infections. HIV-1 codon usage has been previously shown to be different from that of other viruses and man. It is evident that the antiretroviral drugs used to restrict HIV-1 replication also select for resistance variants. We wanted to test whether codon frequencies in HIV-1 sequences from treatment-experienced patients differ from those of treatment-naive individuals due to drug pressure affecting codon usage bias. RESULTS: We developed a JavaScript to determine the codon frequencies of aligned nucleotide sequences. Irrespective of subtypes, using HIV-1 pol sequences from 532 treatment-naive and 52 treatment-experienced individuals, we found that pol sequences from treatment-experienced patients had significantly increased AGA (arginine; p = 0.0002***) and GGU (glycine; p = 0.0001***), and decreased AGG (arginine; p = 0.0001***) codon frequencies. The same pattern was not observed when subtypes B and C sequences were analyzed separately. Additionally, irrespective of subtypes, using HIV-1 gag sequences from 524 treatment-naive and 54 treatment-experienced individuals, gag sequences from treatment-experienced patients had significantly increased CUA (leucine; p < 0.0001***), CAG (glutamine; p = 0.0006***), AUC (isoleucine; p < 0.0001***) and UCU (serine; p = 0.0005***), and decreased AUA (isoleucine; p = 0.0003***) and CAA (glutamine; p = 0.0006***) codon frequencies. CONCLUSION: Using pol and gag genes derived from the same HIV-1 genome, we show that antiretroviral therapy changed certain HIV-1 codon frequencies in a subtype specific way.
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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 | 0.001 | 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) | 0.000 | 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".