No abstract

Inferring the transmission direction between linked individuals living with HIV provides unparalleled power to understand the epidemiology that determines transmission. Phylogenetic ancestral-state reconstruction approaches infer the transmission direction by identifying the individual in whom the most recent common ancestor of the virus populations originated. While these methods vary in accuracy, it is unclear why. To evaluate the performance of phylogenetic ancestral-state reconstruction to determine the transmission direction of HIV-1 infection, we inferred the transmission direction for 112 transmission pairs where transmission direction and detailed additional information were available. We then fit a statistical model to evaluate the extent to which epidemiological, sampling, genetic, and phylogenetic factors influenced the outcome of the inference. Finally, we repeated the analysis under real-life conditions with only routinely available data. We found that whether ancestral-state reconstruction correctly infers the transmission direction depends principally on the phylogeny's topology. For example, under real-life conditions, the probability of identifying the correct transmission direction increases from 32%—when a monophyletic–monophyletic or paraphyletic–polyphyletic tree topology is observed and when the tip closest to the root does not agree with the state at the root—to 93% when a paraphyletic–monophyletic topology is observed and when the tip closest to the root agrees with the root state. Our results suggest that documenting larger differences in relative intrahost diversity increases our confidence in the transmission direction inference of linked pairs for population-level studies of HIV. These findings provide a practical starting point to determine our confidence in transmission direction inference from ancestral-state reconstruction.

Inferring the direction of transmission between linked individuals living with HIV provides unparalleled power to understand the epidemiology that determines transmission. State-of-the-art approaches to infer directionality use phylogenetic ancestral state reconstruction to identify the individual in whom the most recent common ancestor of the virus populations originated. However, these methods vary in their accuracy when applied to different datasets and it is currently unclear under what circumstances inferring directionality is inaccurate and when bias is more likely. To evaluate the performance of phylogenetic ancestral state reconstruction, we inferred directionality for 112 HIV transmission pairs where the direction of transmission was known, and detailed additional information was available. Next, we fit a statistical model to evaluate the extent to which epidemiological, sampling, genetic and phylogenetic factors influenced the outcome of the inference. Third, we repeated the analysis under real-life conditions when only routinely collected data are available. We found that the inference of directionality depends principally on the topology class and branch length characteristics of the phylogeny. Specifically, directionality is most correctly inferred when the phylogenetic diversity and the minimum root-to-tip distance in the transmitter is greater than that of the recipient partner and when the minimum inter-host patristic distance is large. Similarly, under real-life conditions, the probability of identifying the correct transmitter increases from 52%--when a monophyletic-monophyletic or paraphyletic-polyphyletic tree topology is observed, when the sample size in both partners is small and when the tip closest to the root does not agree with the state at the root--to 93% when a paraphyletic-monophyletic topology is observed, when the sample size is large and when the tip closest to the root agrees with the state at the root. Our results support two conclusions. First, that discordance between previous studies in inferring transmission direction can be explained by differences in key phylogenetic properties that arise due to different evolutionary, epidemiological and sampling processes; and second that easily calculated metrics from the phylogenetic tree of the transmission pair can be used to evaluate the accuracy of inferring directionality under real-life conditions for use in population-wide studies. However, given that these methods entail considerable uncertainty, we strongly advise against using these methods for individual pair-level analysis.

Background HIV-1 infections that are initiated by multiple founder variants are characterised by a higher viral load and a worse clinical prognosis, yet little is known about the routes of exposure through which multiple variant transmission is most likely, and whether methods of quantifying the number of founder variants differ in their accuracy. Methods We conducted a systematic review of studies that estimated founder variant multiplicity in HIV-1 infection, searching MEDLINE, EMBASE and Global Health databases for papers published between 1st January 1990 and 14th September 2020 (PROSPERO study CRD42020202672). Leveraging individual patient estimates from these studies, we performed a logistic meta-regression to estimate the probability that an HIV infection is initiated by multiple founder variants. We calculated a pooled estimate using a random effects model, subsequently stratifying this estimate across nine transmission routes in a univariate analysis. We then extended our model to adjust for different study methods in a multivariable analysis, recalculating estimates across the nine transmission routes. Findings We included 71 publications in our analysis, comprising 1664 individual patients. Our pooled estimate of the probability that an infection is initiated by multiple founder variants was 0.25 (95% CI: 0.21-0.30), with moderate heterogeneity (Q=137.1, p<.001, I2=65.3%). Our multivariable analysis uncovered differences in the probability of multiple variant infection by transmission route. Relative to a baseline of male-to-female transmission , the probability for female-to-male multiple variant transmission was significantly lower at 0.10 (95% CI: 0.05-0.21), while the probability for people-who-inject-drugs (PWID) transmission was significantly higher at 0.29 (0.13-0.52). There was no significant difference in the probability of multiple variant transmission between male-to-female transmission (0.16 (0.08-0.29)), post-partum mother-to-child (0.12 (0.02-0.51)), pre-partum mother-to-child (0.13 (0.05-0.32)), intrapartum mother-to-child (0.21 (0.08-0.44)) and men-who-have-sex-with-men (MSM) transmission (0.23 (0.03-0.7)). Interpretation We identified PWID transmissions are significantly more likely to result in an infection initiated by multiple founder variants, whilst female-to-male infections are significantly less likely. Quantifying how the routes of HIV infection impact the transmission of multiple variants allows us to better understand how the evolution and epidemiology of HIV-1 determine the clinical picture. Funding This study was supported by the MRC Precision Medicine Doctoral Training Programme (ref: 2259239) and a ERC Starting Grant awarded to KEA (award number 757688).