ArticleVirus evolution2026
Single genome amplification and molecular cloning of HIV-1 populations in acute HIV-1 infection: implications for studies on HIV-1 diversity and evolutionary rate.
Article in Virus evolution, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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1 citing paper in PubMed.
- Longitudinal Analysis of HIV-2 Proviral DNA Reveals Archived Protease Inhibitor Resistance and Reservoir Evolution over Eight Years.International journal of molecular sciences · 2026Article
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18 authors.
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Abstract
Background: Human immunodeficiency virus type 1 (HIV-1) is one of the fastest-evolving human pathogens. Understanding HIV-1 transmission, within-host adaptation, and evolutionary dynamics is pivotal for development of interventions and vaccines. HIV-1 infection is generally caused by a single transmitted founder virus (TFV), and TFV sequences are typically obtained using single genome amplification (SGA). However, suboptimal sample quality can cause sequencing failures, representing considerable losses considering the scarcity of acute HIV-1 infection (AHI) samples. Sequencing failures may be mitigated by molecular cloning (MC), which can be less vulnerable to sample quality but more susceptible to polymerase chain reaction (PCR) errors. Here, we explore the feasibility of supplementing SGA with MC data using samples from clinical and research cohorts to determine whether sequence diversity and evolutionary rate estimates are comparable between the techniques. Methods: Plasma samples were selected from participants with documented AHI from an East African research cohort (the International AIDS Vaccine Initiative, 2006-2011) and a clinical cohort from Sweden (1983-2011). SGA and MC sequencing were done on the HIV-1 Results: One hundred participants (median age 30.3 years, 15% female), contributing 350 samples from four longitudinal time points, 10-540 days post-infection, met the inclusion criteria. SGA succeeded on 90% of research cohort and 48% of clinical cohort samples. Comparative analysis of linked SGA and MC data from 10 samples indicated that approximately eight sequences were necessary for diversity estimates. Consistently higher sequence diversity was observed among MC relative to SGA sequences (median [IQR]: 0.009 [0.003, 0.015] and 0.004 [0.001, 0.012] substitutions/site, Conclusion: MC data is a suitable supplement for SGA-based HIV-1 studies to preserve the value of precious samples for analysis of evolutionary rate, but not for sequence diversity.
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