Antiretroviral Imprints and Genomic Plasticity of HIV-1 pol in Non-clade B: Implications for Treatment
Bibliographic record
Abstract
Combinational antiretroviral therapy (cART) is the most effective tool to prevent and control HIV-1 infection without an effective vaccine. However, HIV-1 drug resistance mutations (DRMs) and naturally occurring polymorphisms (NOPs) can abrogate cART efficacy. Here, we aimed to characterize the HIV-1polmutation landscape in Cameroon, where highly diverse HIV clades circulate, and identify novel treatment-associated mutations that can potentially affect cART efficacy. More than 8,000 functional Cameroonian HIV-1polsequences from 1987 to 2020 were studied for DRMs and NOPs. Site-specific amino acid frequencies and quaternary structural features were determined and compared between periods before (≤2003) and after (2004–2020) regional implementation of cART. cART usage in Cameroon induced deep mutation imprints in reverse transcriptase (RT) and to a lower extent in protease (PR) and integrase (IN), according to their relative usage. In the predominant circulating recombinant form (CRF) 02_AG (CRF02_AG), 27 canonical DRMs and 29 NOPs significantly increased or decreased in RT during cART scale-up, whereas in IN, no DRM and only seven NOPs significantly changed. The profound genomic imprints and higher prevalence of DRMs in RT compared to PR and IN mirror the dominant use of reverse transcriptase inhibitors (RTIs) in sub-Saharan Africa and the predominantly integrase strand transfer inhibitor (InSTI)-naïve study population. Our results support the potential of InSTIs for antiretroviral treatment in Cameroon; however, close surveillance of IN mutations will be required to identify emerging resistance patterns, as observed in RT and PR. Population-wide genomic analyses help reveal the presence of selective pressures and viral adaptation processes to guide strategies to bypass resistance and reinstate effective treatment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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".