A Direct Comparison of Two Densely Sampled HIV Epidemics: The UK and Switzerland.

Samantha J. Lycett,Mohaned Shilaih,Huldrych F. Günthard,Vincent Aubert,Thomas Klimkait,Emma B. Hodcroft,Alison E. Brown,Sabine Yerly,Jürg Böni,David Dunn,Wan-Lin Yang,Manon L. Ragonnet-Cronin,Esther Fearnhill,Roger Kouyos,Andrew J. Leigh Brown

doi:10.1038/srep32251

Abstract

Phylogenetic clustering approaches can elucidate HIV transmission dynamics. Comparisons across countries are essential for evaluating public health policies. Here, we used a standardised approach to compare the UK HIV Drug Resistance Database and the Swiss HIV Cohort Study while maintaining data-protection requirements. Clusters were identified in subtype A1, B and C pol phylogenies. We generated degree distributions for each risk group and compared distributions between countries using Kolmogorov-Smirnov (KS) tests, Degree Distribution Quantification and Comparison (DDQC) and bootstrapping. We used logistic regression to predict cluster membership based on country, sampling date, risk group, ethnicity and sex. We analysed >8,000 Swiss and >30,000 UK subtype B sequences. At 4.5% genetic distance, the UK was more clustered and MSM and heterosexual degree distributions differed significantly by the KS test. The KS test is sensitive to variation in network scale, and jackknifing the UK MSM dataset to the size of the Swiss dataset removed the difference. Only heterosexuals varied based on the DDQC, due to UK male heterosexuals who clustered exclusively with MSM. Their removal eliminated this difference. In conclusion, the UK and Swiss HIV epidemics have similar underlying dynamics and observed differences in clustering are mainly due to different population sizes.

Highlights

Background sequencesAll pol (HXB2 positions 2253–3870) sequences of HIV subtype A1, B, and C longer than 900 bases were retrieved from Los Alamos National Laboratory (LANL) (January 2014)
HIV pol sequences were retrieved from the Swiss HIV Cohort Study (SHCS) drug resistance database[18] (SHCS DRDB, 2014) and from the UK HIV RDB
Related sequences were obtained from the Los Alamos National Laboratory (LANL) database as described in the Methods

Summary

Introduction

Background sequencesAll pol (HXB2 positions 2253–3870) sequences of HIV subtype A1, B, and C longer than 900 bases were retrieved from LANL (January 2014). To limit the size of alignments, the ten closest sequences to each of the local (UK and Swiss) sequences were selected using Viroblast[34]. Clusters were selected for further analysis if they were supported by bootstrap thresholds of 70%, 80%, 90% and 95% and maximum GD of 1.5% or 4.5% (8 thresholds total)[22]. Of the initially identified clusters, those in the Swiss trees were further selected to contain at least 80% SHCS sequences, and clusters in the UK trees at least 80% UK sequences. We examined all clusters with at least one UK or Swiss sequence (within the respective datasets) to investigate mixing between national and foreign sequences. The automated pipeline included analysis with the Cluster Picker and Cluster Matcher[22] as well as processing through python and R scripts (available upon request). The Cluster Picker was upgraded to recognise IUPAC nucleotide ambiguity codes as matches (version available upon request from the authors), increasing clustering by around 15% in both datasets

Objectives

Methods

Results

Conclusion