CROI 2020 Abstract eBook

Abstract eBook

Poster Abstracts

Conclusion: Clustering similarity between common molecular epidemiology methods varied, with some substantial discordance. In the context of integration of molecular epidemiology into public health, this implies that the choice of clustering method and threshold may impact precision of public health interventions.

to the observed counts within and across populations with the exception of transmissions within the WHR that increased more than four-fold. Conclusion: Majority of HIV-1 transmissions were largely localized within the three studied populations. An estimation of the viral transmission flows suggests that the high-risk FF population considered a hotspot for HIV infection could act as a sink of virus flowing from the GP. Although consistent with our earlier findings, interpretation of these results highlights the importance of correcting for sampling heterogeneity that could underestimate transmission flows. Results further imply that location focused interventions could be key for effective epidemic control in high-risk populations but should not negate the need for broader prevention.

930 USE OF PHYLOGENETIC ANALYSIS TO INFER THE DIRECTION OF HIV TRANSMISSION Yinfeng Zhang 1 , Chris Wymant 2 , Oliver Laeyendecker 1 , Kate Grabowski 1 , Matthew D. Hall 2 , Sarah E. Hudelson 1 , Estelle Piwowar-Manning 1 , Marybeth McCauley 3 , Johnstone Kumwenda 4 , Mina C. Hosseinipour 5 , Ying Qing Chen 6 , Myron S. Cohen 5 , Christophe Fraser 2 , Susan H. Eshleman 1 , for the HPTN 052 Study Team 1 Johns Hopkins University School of Medicine, Baltimore, MD, USA, 2 University of Oxford, Oxford, UK, 3 FHI 360, Washington, DC, USA, 4 Malawi College of Medicine- Johns Hopkins University Research Project, Blantyre, Malawi, 5 University of North Carolina at Chapel Hill, Chapel Hill, NC, USA, 6 Fred Hutchinson Cancer Research Center, Seattle, WA, USA Background: Phylogenetic analysis can provide important information about the spread of HIV in cohorts and populations. Methods are well established for identifying genetically-linked viral infections and clusters. Improved methods are needed to infer the direction of HIV transmission. We used next-generation sequencing (NGS) to generate whole-genome HIV sequences from couples with known linked HIV infection and known transmission direction. These data were used to evaluate methods for inferring the direction of HIV transmission. Methods: NGS was performed using samples from 32 index-partner pairs (couples) enrolled in the HIV Prevention Trials Network (HPTN) 052 trial (up to two samples per person, collected on different dates). Index samples were obtained up to 5.5 years before partner infection; partner samples were obtained near the time of HIV seroconversion. The bioinformatics method, phyloscanner, was used to infer transmission direction. We evaluated inferred transmission direction using whole-genome NGS data for individual couples, for all couples as a group (one sample/person; group analysis) and for all couples using all available samples (multi-sample group analysis). We also evaluated inferred transmission direction using NGS data from individual HIV genes (gag, pol, env). Results: Ultra-deep whole-genome NGS data was obtained for 116 samples from indexes and partners, including 105 unique index-partner sample pairs. Transmission direction was correctly inferred (index to partner) for 98/105 (93.3%) of the individual sample pairs, 99/105 (94.3%) of the sample pairs using group analysis, and 31 (96.9%) of the 32 couples using multi-sample group analysis. For the remaining cases, linkage was established but transmission direction could not be inferred. There were no cases where the incorrect transmission direction (partner to index) was inferred. The methods were more likely to infer transmission direction when there was a longer time between index and partner sample collection. Pol region sequences performed better than env or gag sequences for inferring transmission direction. Conclusion: Accurate predictions of transmission direction were obtained using whole-genome and pol NGS data. Further research is needed to evaluate the performance of these methods in other settings and cohorts and in cases where both individuals (source and recipient) have long-term infection. 931 NEAR REAL-TIME IDENTIFICATION OF RECENT HIV INFECTION BY PID-NGS IN NORTH CAROLINA Shuntai Zhou 1 , Sabrina Clark 1 , Matthew Moeser 1 , Scott J. Zimmerman 2 , Erika Samoff 2 , Victoria L. Mobley 2 , Andrew E. Cressman 1 , Simon Frost 3 , Joseph J. Eron 1 , Michael Clark 1 , Corbin Jones 1 , Myron S. Cohen 1 , Julie A.Nelson 1 , Ronald Swanstrom 1 , Ann Dennis 1 1 University of North Carolina at Chapel Hill, Chapel Hill, NC, USA, 2 North Carolina State Laboratory of Public Health, Raleigh, NC, USA, 3 Cambridge University, Cambridge, UK

Poster Abstracts

929 CONCORDANCE OF METHODS IN IDENTIFYING MOLECULAR HIV CLUSTERS Vladimir Novitsky 1 , Jon Steingrimsson 1 , Mark Howison 1 , Fizza S. Gillani 1 , Yuanning Li 2 , Akarsh Manne 1 , Matthew C. Spence 3 , Theodore Marak 3 , Philip A. Chan 1 , Thomas Bertrand 3 , Utpala Bandy 3 , Nicole Alexander-Scott 3 , Casey Dunn 2 , Joseph Hogan 1 , Rami Kantor 1 1 Brown University, Providence, RI, USA, 2 Yale University, New Haven, CT, USA, 3 Rhode Island Department of Health, Providence, RI, USA Background: Molecular epidemiology is increasingly used to understand HIV transmission and monitor outbreaks and can be a critical tool in their prevention. Different methods are available to identify transmission networks, and their choice can be arbitrary. The impact of method choice on cluster identification has not been comprehensively assessed and may affect public health interventions that rely upon their results. Methods: We studied 8 commonly used methods (7 model-based, and distance-based HIV-TRACE) and used them to identify clusters of HIV-1 subtype B pol sequences from 1,656 persons, ~80% of a densely-sampled Rhode Island epidemic during 2004-2018. For each method, we compared proportion of clustered sequences within and between methods using various distance and bootstrap thresholds; and clustering concordance between methods (including percentages of identical sequence pairs that cluster together; percentages of cluster similarity at 100% and at 80%; and percentages of identical non-clustered sequences). We conducted comparisons under (i) strict (bootstrap ≥0.95; TN93 distance 0.015 substitutions/site) and (ii) relaxed (bootstrap 0.8-0.9; distance 0.03-0.045 substitutions/site) thresholds, intended to address different public health objectives (e.g. strict for outbreak; relaxed for epidemic characterization). Results: Of the 1,656 sequences, 18-53% formed 114-217 clusters, depending on thresholds used. Clustering proportion within methods depended on bootstrap and distance, with distance having stronger effects. Variation in clustering proportion across methods was more pronounced with stringent bootstraps and relaxed distances. For strict thresholds, HIV-TRACE identified 5-15% higher proportion of clustered individuals than model-based methods (p<0.005 for all pairwise comparisons). In contrast, for relaxed thresholds, HIV-TRACE identified 3-19% lower proportion of clustered individuals than model-based methods (p<0.05 for all pairwise comparisons). Distributions of percent concordance between methods, stratified by threshold type (strict, relaxed), are presented in the Table.

CROI 2020 349