Prevalence and clinical impacts of HIV-1 intersubtype recombinants in Uganda revealed by near-full-genome population and deep sequencing approaches
Bibliographic record
Abstract
OBJECTIVES: HIV-1 subtypes A1 and D cocirculate in a rural community in Mbarara, Uganda. This study examines HIV-1 intersubtype recombination in this community under a full-genome sequencing context. We aim to estimate prevalence, examine time trends, and test for clinical correlates and outcomes associated with intersubtype recombinants. METHODS: Near-full-genome HIV-1 Sanger sequence data were collected from plasma samples of 504 treatment-naïve individuals, who then received protease inhibitor or nonnucleoside reverse transcriptase inhibitor-containing regimens and were monitored for up to 7.5 years. Subtypes were inferred by Los Alamos Recombinant Identification Program (RIP) 3.0 and compared with Sanger/REGA and MiSeq/RIP. 'Nonrecombinants' and 'recombinants' infections were compared in terms of pretherapy viral load, CD4 cell count, posttherapy time to virologic suppression, virologic rebound, first CD4 rise above baseline and sustained CD4 recovery. RESULTS: Prevalence of intersubtype recombinants varied depending on the genomic region examined: gag (15%), prrt (11%), int (8%), vif (10%), vpr (2%), vpu (9%), GP120 (8%), GP41 (18%), and nef (4%). Of the 200 patients with near-full-genome data, prevalence of intersubtype recombination was 46%; the most frequently observed recombinant was A1-D (25%). Sanger/REGA and MiSeq/RIP yielded generally consistent results. Phylogenetic tree revealed most recombinants did not share common ancestors. No temporal trend was observed (all P > 0.1). Subsequent subtype switches were detected in 27 of 143 (19%) study participants with follow-up sequences. Nonrecombinant versus recombinants infections were not significantly different in any pre nor posttherapy clinical correlates examined (all P > 0.2). CONCLUSION: Intersubtype recombination was highly prevalent (46%) in Uganda if the entire HIV genome was considered, but was neither associated with clinical correlates nor therapy outcomes.
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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) | 0.000 | 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".