Abstract
Lymphoid tissue is a key reservoir established by HIV-1 during acute infection. It is a site associated with viral production, storage of viral particles in immune complexes, and viral persistence. Although combinations of antiretroviral drugs usually suppress viral replication and reduce viral RNA to undetectable levels in blood, it is unclear whether treatment fully suppresses viral replication in lymphoid tissue reservoirs. Here we show that virus evolution and trafficking between tissue compartments continues in patients with undetectable levels of virus in their bloodstream. We present a spatial and dynamic model of persistent viral replication and spread that indicates why the development of drug resistance is not a foregone conclusion under conditions in which drug concentrations are insufficient to completely block virus replication. These data provide new insights into the evolutionary and infection dynamics of the virus population within the host, revealing that HIV-1 can continue to replicate and replenish the viral reservoir despite potent antiretroviral therapy.
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Acknowledgements
We thank G. J. Beilman, A. Thorkelson, P. Swantek, K. Mars and K. Kunstman for their technical assistance. We thank E. Domingo and T. Bhattacharya for their constructive and informed review. We are indebted to the patients who participated in this study. This work was supported by the National Institutes of Health (DA033773 to S.M.W., AI1074340 to T.W.S., and GM110749 to S.L.K.P.), the Medical Research Council (G1000196 to M.H.M.), the Framework Programme for Research and Technological Development (278433-PREDEMICS to A.R.) and the European Research Council (260864 to A.R.). The Oxford Martin School supports H.R.F. All Souls College supports A.R.M. where S.M.W. held a Visiting Fellowship. A Newton International Fellowship from the Royal Society supported T.B. A Wellcome Trust Investigator award supported M.H.M. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
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T.W.S., A.T.H., and S.M.W. conceived the experiments and designed the study. T.W.S. and J.G.C. acquired the patient tissue samples. A.T.H. performed the in situ hybridization experiments. C.V.F. measured the intracellular drug concentrations. R.L.-R., E.-Y.K., and Y.-S.C. generated the viral sequences. R.L.-R., T.B., E.-Y.K., S.P., M.H.M., S.L.K.P., A.R., and S.M.W. analysed the data. R.L.-R., T.B., J.A., and A.R. conducted the Bayesian inference analyses. H.R.F., A.R.M., and S.M.W. developed the spatial and dynamic model. R.L.-R., T.B., H.R.F., A.T.H., S.L.K.P., A.R.M., A.R., and S.M.W. wrote the paper, with extensive input from all authors. All authors discussed the results and commented on the manuscript.
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Extended data figures and tables
Extended Data Figure 1 The amount of virus and concentration of drugs measured during antiretroviral therapy.
a–c, The changes in copies of HIV-1 RNA per ml of blood, the number of the HIV-1 RNA particles bound to the follicular dendritic cell network per gram of lymphoid tissue, and the number HIV-1 RNA positive cells per gram of lymphoid tissue over the first 6 months of treatment in subjects 1774, 1727 and 1679 (a, b, and c, respectively). Filled circles represent detectable measures. Unfilled circles represent undetectable measures and are plotted at the limit of detection. d–f, Antiretroviral drug concentrations in cells from lymph node (dashed line) or blood (solid line) in subjects 1774, 1727 and 1679 (d, e and f, respectively; see Methods). Intracellular TFV-diphosphate (TFV-DP) concentrations (fmol per 106 cells) are shown in orange, FTC-triphosphate (FTC-TC) (fmol per 106 cells) in green, ATV (ng ml−1) in purple, and EFV (ng ml−1) in blue. Samples with concentrations that were below the limits of quantification (2.5 fmol per 106 cells, 2.5 fmol per 106 cells, 0.014 ng ml−1 and 0.063 ng ml−1, respectively) were assigned a value of 1 for graphical illustration purposes.
Extended Data Figure 2 Phylogenies and Highlighter plots for the Gag region of HIV-1.
a–c, Maximum-likelihood trees were constructed using gene sequences from the Gag region of HIV-1 from lymph node and blood before and after the guanosines within all possible APOBEC3 trinucleotide sequence context of edited sites were masked in the alignments, regardless of their presence in hypermutated or non-hypermutated sequences, to avoid their distortion in the phylogenetic reconstructions. Branch tips are coloured according to compartment sampled: red for plasma, gold for lymph node, and blue for blood. The progressive shading of the colours of the branch tips indicate the points in time sampled. Phylogenetic trees reconstructed from the haplotypes in which the guanosines in the APOBEC3 trinucleotide context of the edited sites are masked in the alignments correct the skewing effect caused by clustering of shared haplotypes that harbour repetitive G-to-A substitutions and longer branch lengths caused by a larger number of these mutations in the hypermutated sequences while retaining the phylogenetic information. The horizontal scale indicates the expected number of substitutions per nucleotide site per unit time, with haplotypes from later time points having diverged more. The Highlighter plots show the haplotypes from the lymphoid tissue and blood time point clusters aligned to the plasma virus sequence from day 0. The particular nucleotide changes are colour-coded in the alignment (thymidine, red; adenosine, green; cytosine, blue; and guanosine, orange). Magenta circles represent APOBEC3-induced G-to-A change in a trinucleotide context of the edited sites, which are distinguishable from the more random error-prone viral reverse transcriptase and RNA polymerase II replicating enzyme induced mutations6. Gene sequences from the Gag region of HIV-1 from subject 1774, who continued to have measureable amounts of HIV-1 RNA in plasma on treatment, and subjects 1727 and 1679 who were well-suppressed on treatment (a, b and c, respectively) before and after the guanosines within the particular APOBEC3 trinucleotide sequence context of edited sites were masked in the entire sequence alignment (left and right panels, respectively).
Extended Data Figure 3 Phylogenies and Highlighter plots for the Pol region of HIV-1.
a–c, Maximum-likelihood trees were constructed using gene sequences from the Pol region (reverse transcriptase (Pol2)) of HIV-1 from lymph node and blood before and after the guanosines within all possible APOBEC3 trinucleotide sequence context of edited sites were masked in the alignments, regardless of their presence in hypermutated or non-hypermutated sequences, to avoid their distortion in the phylogenetic reconstructions. Branch tips are coloured according to compartment sampled: red for plasma; gold for lymph node; and blue for blood. The progressive shading of the colours of the branch tips indicate the points in time sampled. The horizontal scale indicates the expected number of substitutions per nucleotide site per unit time with haplotypes from later time points having diverged more. The Highlighter plots show the haplotypes from the lymphoid tissue and blood time point clusters aligned to the plasma virus sequence from day 0. The particular nucleotide changes are colour-coded in the alignment (thymidine, red; adenosine, green; cytosine, blue; and guanosine, orange). Magenta circles represent APOBEC3-induced G-to-A change in a trinucleotide context of the edited sites. Gene sequences from the Pol region of HIV-1 that spanned the genomic region encoding the viral enzyme reverse transcriptase from subjects 1774, 1727, and 1679 (a, b and c, respectively) before and after the guanosines within the particular APOBEC3 trinucleotide sequence context of edited sites were masked in the entire sequence alignment (left and right panels, respectively).
Extended Data Figure 4 Alternative drug-dependent fitness landscape plots.
a, Fitness landscape plot for a partially drug-resistant strain. This strain confers a low level of drug resistance relative to the replicative fitness cost imposed by the resistance mutations. The drug-resistant strain (blue line) does not out-compete the drug-sensitive strain (orange line) at any effective treatment concentration where it can grow. There are two phases to the dynamics: at lower effective drug concentrations (left of grey line), the drug-sensitive strain thrives; beyond this threshold, neither strain can continuously replicate. b, Fitness landscape plot for a highly drug-resistant strain. This strain confers a high-level of drug resistance relative to the replicative fitness cost imposed by the resistance mutations. At low effective drug concentrations (left of grey line), the drug-sensitive strain out-competes the drug-resistant strain. At high effective drug concentrations, the drug-resistant strain out-competes the drug-sensitive strain and can continuously replicate. We argue that, typically, highly drug-resistant mutants of this sort neither exist in the viral population of patients before treatment, nor arise through random mutation during the course of antiretroviral therapy (see Supplementary Information and Supplementary Table 2). Drug-resistant strains that are capable of ongoing replication at high effective drug concentrations are not typically generated in individuals because: they are generated in a single step very rarely; and stepwise generation from partially resistant strains is also rare because partially resistant strains are out-competed in the sanctuary site that constantly replenishes the pool. The strain-specific effective reproductive numbers for the drug-sensitive (orange line) and drug-resistant (blue line) strains are shown. The orange sections highlight regions of parameter space where the drug-sensitive strain will dominate, and the blue section highlights regions of parameter space where the drug-resistant strain will dominate. For simplicity, only the impact of changes to the effectiveness of a single drug in a single compartment is shown.
Extended Data Figure 5 Model of replication dynamics and treatment effectiveness in the viral reservoir fitted to the data.
The model is fitted to the total inferred average body counts of free virus particles (green line), infected CD4+ T cells (orange line) and virus bound to the follicular dendritic cell network of B-cell follicles (grey line). a, Dynamics over the first 200 days of treatment. Note that early in antiretroviral therapy, HIV-1 RNA in plasma declines more rapidly than virus bound to the follicular dendritic cell network of B-cell follicles. Circles demonstrate average data from the three patients discussed in detail in this study and an additional nine patients presented elsewhere13. Where the average value was indeterminate because of test sensitivity, the data are fitted below the upper limit of the average log10 infectious units. The range below the upper limit is represented by a vertical bar. b, Dynamics over a longer period. The model predicts the persistent low-level viral RNA in plasma. (see Supplementary Information). The optimal model fit parameters are presented in Supplementary Table 1.
Supplementary information
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Lorenzo-Redondo, R., Fryer, H., Bedford, T. et al. Persistent HIV-1 replication maintains the tissue reservoir during therapy. Nature 530, 51–56 (2016). https://doi.org/10.1038/nature16933
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DOI: https://doi.org/10.1038/nature16933
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