Pillay, D. | Search bioRxiv

bioRxiv2017

A High HIV-1 Strain Variability in London, UK, Revealed by Full-Genome Analysis: Results from the ICONIC Project

Background & MethodsThe ICONIC project has developed an automated high-throughput pipeline to generate HIV nearly full-length genomes (NFLG, i.e. from gag to nef) from next-generation sequencing (NGS) data. The pipeline was applied to 420 HIV samples collected at University College London Hospital and Barts Health NHS Trust (London) and sequenced using an Illumina MiSeq at the Wellcome Trust Sanger Institute (Cambridge). Consensus genomes were generated and subtyped using COMET, and unique recombinants were studied with jpHMM and SimPlot. Maximum-likelihood phylogenetic trees were constructed using RAxML to identify transmission networks using the Cluster Picker.\n\nResultsThe pipeline generated sequences of at least 1Kb of length (median=7.4Kb) for 375 out of the 420 samples (89%), with 174 (46.4%) being NFLG. A total of 365 sequences (169 of them NFLG) corresponded to unique subjects and were included in the down-stream analyses. The most frequent HIV subtypes were B (n=149, 40.8%) and C (n=77, 21.1%) and the circulating recombinant form CRF02_AG (n=32, 8.8%). We found 14 different CRFs (n=66, 18.1%) and multiple URFs (n=32, 8.8%) that involved recombination between 12 different subtypes/CRFs. The most frequent URFs were B/CRF01_AE (4 cases) and A1/D, B/C, and B/CRF02_AG (3 cases each). Most URFs (19/26, 73%) lacked breakpoints in the PR+RT pol region, rendering them undetectable if only that was sequenced. Twelve (37.5%) of the URFs could have emerged within the UK, whereas the rest were probably imported from sub-Saharan Africa, South East Asia and South America. For 2 URFs we found highly similar pol sequences circulating in the UK. We detected 31 phylogenetic clusters using the full dataset: 25 pairs (mostly subtypes B and C), 4 triplets and 2 quadruplets. Some of these were not consistent across different genes due to inter- and intra-subtype recombination. Clusters involved 70 sequences, 19.2% of the dataset.\n\nConclusionsThe initial analysis of genome sequences detected substantial hidden variability in the London HIV epidemic. Analysing full genome sequences, as opposed to only PR+RT, identified previously undetected recombinants. It provided a more reliable description of CRFs (that would be otherwise misclassified) and transmission clusters.

genomics↗

bioRxiv2017

External introductions helped drive and sustain the high incidence of HIV-1 in rural KwaZulu-Natal, South Africa

Despite increasing access to antiretroviral therapy, HIV incidence in rural KwaZulu-Natal communities remains among the highest ever reported in Africa. While many epidemiological factors have been invoked to explain this high incidence, widespread human mobility and movement of viral lineages between geographic locations have implicated high rates of transmission across communities. High rates of crosscommunity transmission call into question how effective increasing local coverage of antiretroviral therapy will be at preventing new infections, especially if many new cases arise from external introductions. To help address this question, we use a new phylodynamic modeling approach to estimate both changes in epidemic dynamics through time and the relative contribution of local transmission versus external introductions to overall incidence from HIV-1 subtype C phylogenies. Our phylodynamic estimates of HIV prevalence and incidence are remarkably consistent with population-based surveillance data. Our analysis also reveals that early epidemic dynamics in this population were largely driven by a wave of external introductions. More recently, we estimate that anywhere between 20-60% of all new infections arise from external introductions from outside the local community. These results highlight the power of using phylodynamic methods to study generalized HIV epidemics and the growing need to consider larger-scale regional transmission dynamics above the level of local communities when designing and testing prevention strategies.

epidemiology↗