Nucleoside and Nucleotide Analogs Select in Culture for Different Patterns of Drug Resistance in Human Immunodeficiency Virus Types 1 and 2
Bibliographic record
Abstract
Recent findings suggest bidirectional antagonisms between the K65R mutation and thymidine analogue mutations in human immunodeficiency virus type 1 (HIV-1)-infected, treatment-experienced patients, yet little is known about HIV-2 in this regard. This study addressed the effects of innate polymorphisms in HIV-2 on emergent resistance to nucleoside/nucleotide analogues. Emergent drug resistance profiles in HIV-2 subtypes A (n = 3) and B (n = 1) were compared to those of HIV-1 subtypes B and C. Drug resistance was evaluated with cord blood mononuclear cells (CBMCs) and MT2 cells, using selective pressure with tenofovir (TFV), zidovudine (ZDV), stavudine (d4T), didanosine (ddI), abacavir (ABC), lamivudine (3TC), emtricitabine (FTC), or various dual-drug combinations. Resistance was evaluated using conventional and ultrasensitive sequencing approaches. In agreement with our previous findings, dual-drug combinations of TFV, ddI, ABC, d4T, ZDV, and 3TC preferentially selected for K65R in HIV-1 subtype C isolates. In HIV-1 subtype B, TFV-3TC and ZDV-3TC selected for M184I and D67N, respectively. In contrast, selections with all four HIV-2 cultures favored the development of M184I in dual-drug combinations that included either 3TC or FTC. Since HIV-2 cultures did not develop K65R, an ultrasensitive allele-specific real-time PCR assay was developed to distinguish the presence of 65R from wild-type K65 after 16 cycles with a discriminatory ability of 0.1% against a population of wild-type virus. These results underscore potential differences in emergent drug resistance pathways in HIV-1 and HIV-2 and show that polymorphisms may influence the development of the resistance pathways that are likely to emerge.
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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".