Highly diversified multiply drug-resistant HIV-1 quasispecies in PBMCs: a case report
Bibliographic record
Abstract
BACKGROUND: Although drug resistance is a major challenge in HIV therapy, the effect of drug resistance mutations on HIV evolution in vivo is not well understood. We have now investigated genetic heterogeneity in HIV-1 by performing drug resistance genotyping of the PR-RT regions of viruses derived from plasma and peripheral blood mononuclear cells (PBMCs) of a single patient who had failed multiple regimens of anti-retroviral therapy. RESULTS: Patterns of drug resistance mutations showed that the viral populations in PBMCs were more heterogeneous than in plasma. Extensive analysis of HIV from infected PBMCs in this patient showed that high-level diversity existed among 109 cloned PR-RT sequences and that the majority of mutations were related to drug resistance. Moreover, the PBMCs included archival species that reflected the treatment history of the patient while those in plasma were mainly related to the most recent treatment. Some of the proviral clones contained single or multiple mutations in various combinations. Approximately eighteen percent of the proviral clones derived from infected PBMCs were defective, i.e. 5.5% contained single nucleotide deletions (frameshift mutations) and 12.8% encoded in-frame stop codons (nonsense mutations). Amino acid substitutions in PR and the polymerase region of RT occurred in 12-15% of cases but were much less frequent in the RNase H region of RT, which might not have been under drug selection pressure. CONCLUSION: Selective drug pressure can yield multiple drug-resistant quasispecies that include archival and replication-incompetent species in PBMC reservoirs.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".