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. 2021 Aug 19:12:719153.
doi: 10.3389/fmicb.2021.719153. eCollection 2021.

Proviral Turnover During Untreated HIV Infection Is Dynamic and Variable Between Hosts, Impacting Reservoir Composition on ART

Kelsie Brooks  1 , F Harrison Omondi  2   3 , Richard H Liang  3 , Hanwei Sudderuddin  3 , Bradley R Jones  3   4 , Jeffrey B Joy  3   4   5 , Chanson J Brumme  3   5 , Eric Hunter  1   6 , Zabrina L Brumme  2   3
Affiliations

Proviral Turnover During Untreated HIV Infection Is Dynamic and Variable Between Hosts, Impacting Reservoir Composition on ART

Kelsie Brooks et al. Front Microbiol. .

Abstract

Human immunodeficiency virus (HIV) can persist as an integrated provirus, in a transcriptionally repressed state, within infected cells. This small yet enduring pool of cellular reservoirs that harbor replication-competent HIV is the main barrier to cure. Entry of viral sequences into cellular reservoirs begins shortly after infection, and cells containing integrated proviral DNA are extremely stable once suppressive antiretroviral therapy (ART) is initiated. During untreated HIV infection however, reservoir turnover is likely to be more dynamic. Understanding these dynamics is important because the longevity of the persisting proviral pool during untreated infection dictates reservoir composition at ART initiation. If the persisting proviral pool turns over slowly pre-ART, then HIV sequences seeded into it during early infection would have a high likelihood of persisting for long periods. However, if pre-ART turnover was rapid, the persisting proviral pool would rapidly shift toward recently circulating HIV sequences. One-way to estimate this turnover rate is from the age distributions of proviruses sampled shortly after therapy initiation: this is because, at the time of sampling, the majority of proviral turnover would have already occurred prior to ART. Recently, methods to estimate a provirus' age from its sequence have made this possible. Using data from 12 individuals with HIV subtype C for whom proviral ages had been determined phylogenetically, we estimated that the average proviral half-life during untreated infection was 0.78 (range 0.45-2.38) years, which is >15 times faster than that of proviral DNA during suppressive ART. We further show that proviral turnover during untreated infection correlates with both viral setpoint and rate of CD4+ T-cell decline during this period. Overall, our results support dynamic proviral turnover pre-ART in most individuals, which helps explain why many individuals' reservoirs are skewed toward younger HIV sequences. Broadly, our findings are consistent with the notion that active viral replication creates an environment less favorable to proviral persistence, while viral suppression creates conditions more favorable to persistence, where ART stabilizes the proviral pool by dramatically slowing its rate of decay. Strategies to inhibit this stabilizing effect and/or to enhance reservoir turnover during ART could represent additional strategies to reduce the HIV reservoir.

Keywords: HIV; persistence; proviral half-life; reservoir; within-host phylogenetic analysis.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The handling editor declared a past collaboration with the authors ZB and CB.

Figures

FIGURE 1
FIGURE 1
Participant sampling timeline. Infection and sampling timelines for the 12 study participants, sorted by their untreated infection duration, and using ART initiation as the reference time-point. IDs ending in “M” and “F” denote male and female participants, respectively. The short vertical line denotes the estimated date of infection, and the gray shaded area denotes ART. The three colored circles denote the dates when plasma HIV RNA env sequences were sampled: at seroconversion (red circle), 1 year after infection (black circle) and before ART (blue circle). The colored diamonds denote when proviral sequences were sampled on ART. Figure adapted from Brooks et al. (2020).
FIGURE 2
FIGURE 2
Estimating the ages of proviruses persisting on ART: Participant Z634F. (A) Sampling timeline for participant Z634F. Plasma HIV RNA env sequences were sampled at three time points between infection and ART (colored circles); proviral sequences were sampled twice on ART (colored diamonds). Gray shading denotes ART. (B) Within-host maximum-likelihood phylogeny inferred from intact, distinct HIV sequences, where the root represents the inferred transmitted/founder virus and the symbols at the tree tips denote the sequence type (plasma HIV RNA versus proviral) and sampling time-point. (C) The dashed blue line represents the linear regression relating the root-to-tip distances of the plasma HIV RNA sequences to their sampling times. The slope of the regression line represents the evolutionary rate (ER) of plasma HIV RNA env sequences in this participant during untreated infection (3.2 × 10− 5 estimated nucleotide substitutions/site/day); this line is used to convert the root-to-tip distances of proviral sequences sampled on ART to their original “creation” (i.e., integration) dates. The faint gray lines represent the underlying evolutionary relationships between sampled HIV sequences. (D) Point estimates and associated 95% CIs of the integration dates of sampled proviral sequences, as inferred from the regression, colored by sampling date. (E) Proportion of Z634F’s proviruses that dated to each year prior to ART.
FIGURE 3
FIGURE 3
Using a mathematical model of HIV infection to demonstrate how untreated HIV infection duration and proviral decay rate shape proviral composition on ART. (A) Model-produced plasma viral load dynamics in a hypothetical individual who initiated ART 3 years after infection (blue line), where proviral DNA was sampled 1 year later (inverted triangle). Note that the proviral sampling time-point is illustrative only: as proviral seeding in the model ceases at ART initiation but decay proceeds at a constant rate, and because proviral compositions are depicted as proportions of proviruses remaining from each year of creation, model predictions would be the same regardless of when the proviral pool is sampled on ART. (B) Model-predicted proviral compositions on ART for the hypothetical individual shown in A, depicted in terms of the proportions of proviruses remaining from each year of creation, under conditions of no decay (purple line), under slow decay rates of 140 months (gray line) and 44 months (dotted black line) that represent published half-lives of proviral DNA and the replication-competent reservoir during ART, respectively (Siliciano et al., 2003; Golob et al., 2018), and under a rapid decay rate of 6 months (red line). (C) Same as panel A, but for a hypothetical individual who initiated ART 7 years after infection. (D) Model predicted proviral compositions on ART for the hypothetical individual shown in C, under various rates of decay.
FIGURE 4
FIGURE 4
Best-fitting proviral decay rates inferred from participants’ proviral compositions on ART (A–L). Each participant’s proviral distribution on ART, as determined phylogenetically via the procedure outlined in Figure 2, is depicted as histograms that show the proportions of proviruses remaining from each year of integration. For the four participants whose proviral pools were sampled twice on ART (N133M, Z634F, Z1165M, and Z1788F), proviral composition is shown as stacked bars. The solid and dashed red lines represent the best-fit half-life and associated 95% confidence intervals, respectively, estimated using a Poisson generalized linear model. Participants are sorted by study ID.
FIGURE 5
FIGURE 5
Comparison of estimated pre-ART proviral decay rates with published rates of reservoir and proviral decay on ART. Estimated pre-ART proviral half-lives and associated 95% CI are shown for the 12 participants, alongside published rates of reservoir (Siliciano et al., 2003) and proviral (Golob et al., 2018) decay on ART. Bi-colored circles represent the four participants for whom proviral sampling was performed twice on ART. Arrowheads indicate upper 95% CIs of infinity.
FIGURE 6
FIGURE 6
Higher pVL setpoint and rapid rate of CD4+ T-cell decline is associated with faster proviral turnover during untreated infection. Spearman’s correlation showing inverse relationship between model-estimated proviral half-lives and participant set point plasma viral load (A), and rate of CD4+ T-cell decline (B). Blue and red dots denote male and female participants, respectively.
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