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
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Background
Human immunodeficiency virus type 1 (HIV-1) is one of the fastest evolving human pathogens. Understanding transmission, within-host adaptation, and evolutionary dynamics are pivotal for development of interventions and vaccines. HIV-1 infection is generally caused by one single transmitted founder virus (TFV), and TFV sequences have typically been obtained using single genome amplification (SGA). However, suboptimal sample quality can result in sequencing failures, representing non-trivial losses considering the scarcity of acute HIV-1 infection (AHI) samples. Sequencing failures may be mitigated by molecular cloning (MC), a method that can be less vulnerable to sample quality but more susceptible to 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 two techniques.
Methods
Participants were enrolled in an East African research cohort from the International AIDS Vaccine Initiative 2006-2011 or a clinical cohort from Sweden (1983-2011). SGA and MC sequencing were done on the HIV-1 env V1-V3 region (approximately 940 base pairs). Within-host sequence diversity was determined from maximum likelihood phylogenetic trees and evolutionary rate by Bayesian phylogenetic analysis. Highlighter and Poisson-Fitter tools, Hamming distances, and assessment of star phylogenies were used to quantify TFVs.
Results
Participants with AHI (N=100, median age 30.3 years, 15% female) were included, contributing 350 samples from four longitudinal time points 10-540 days post infection. 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 (mean±SD 0.009±0.007 and 0.006±0.006 substitutions/site, p<0.001), whereas evolutionary rates were similar between the two methods (mean±SD 0.014±0.006 vs. 0.014±0.009 substitutions/site/year, p=0.673). Five participants with visits within 45 days post infection were eligible for TFV quantification and all found to have one TFV using both MC and SGA data.
Conclusion
MC data is a suitable supplement for SGA-based studies to preserve the value of precious samples for evolutionary rate but not sequence diversity analysis.
