Addressing an HIV cure in LMIC

HIV-1 persists in infected individuals despite years of antiretroviral therapy (ART), due to the formation of a stable and long-lived latent viral reservoir. Early ART can reduce the latent reservoir and is associated with post-treatment control in people living with HIV (PLWH). However, even in post-treatment controllers, ART cessation after a period of time inevitably results in rebound of plasma viraemia, thus lifelong treatment for viral suppression is indicated. Due to the difficulties of sustained life-long treatment in the millions of PLWH worldwide, a cure is undeniably necessary. This requires an in-depth understanding of reservoir formation and dynamics. Differences exist in treatment guidelines and accessibility to treatment as well as social stigma between low- and-middle income countries (LMICs) and high-income countries. In addition, demographic differences exist in PLWH from different geographical regions such as infecting viral subtype and host genetics, which can contribute to differences in the viral reservoir between different populations. Here, we review topics relevant to HIV-1 cure research in LMICs, with a focus on sub-Saharan Africa, the region of the world bearing the greatest burden of HIV-1. We present a summary of ART in LMICs, highlighting challenges that may be experienced in implementing a HIV-1 cure therapeutic. Furthermore, we discuss current research on the HIV-1 latent reservoir in different populations, highlighting research in LMIC and gaps in the research that may facilitate a global cure. Finally, we discuss current experimental cure strategies in the context of their potential application in LMICs

Replication capacity of viruses from acute infection drives HIV-1 disease progression.

CAPRISA, 2017.Abstract available in pdf

Cervicovaginal inflammation facilitates acquisition of less infectious HIV variants.

CAPRISA, 2017.Abstract available in pdf

Entry of X4 Viruses Into the Pool of Long-Lived Cells.

A. OGVs were tested for use of CCR5 and CXCR4 for coreceptor as a function of their sensitivity to maraviroc and AMD-3100, respectively. The graph shows the amount of viral infectivity for each OGV that was blocked by AMD-3100, indicating dependence on CXCR4 for infectivity. OGVs from CAP257 and CAP 316 showed a high percentage of viruses that required CXCR4 for efficient entry, with OGVs from other participants showing no ability to use CXCR4 and only CCR5 usage. B. All viral sequences (from pretherapy replicating virus and from proviral DNA and OGVs in the pool of long-lived cells) were plotted as a function of their False Positive Rate (FPR) value as determined based on the env V3 sequence analyzed using Geno2Pheno (https://coreceptor.geno2pheno.org/). The horizontal lines represent individual time points of replicating viral sequences (negative values for Weeks on ART) with the FPR value for each sequence color-coded for time (as in Fig 1), or viral sequences from the pool of long-lived cells as either proviral DNA (black dots) or OGVs (as magenta dots). CAP217 is shown as a control who did not develop X4 viruses prior to the initiation of therapy. CAP257 and CAP316 developed X4 viruses close to the time of therapy initiation. FPR values of less than 2 are considered reliably identified as being able to use CXCR4 for entry, with some viruses retaining the ability to still enter using CCR5 (termed dual-tropic) while some have lost the ability to use CCR5 (pure X4). FPR values greater than 10 are considered to reliably identify viruses that only use CCR5. FPR values between 2 and 10 are considered indeterminant in that they represent some viruses that can use CXCR4 and others that use only CCR5 making phenotypic calls for viruses with this range of FPR values unreliable. The graphs show that viruses that were circulating at the time of therapy initiation entered the pool of long-lived cells regardless of entry phenotype.</p

Timing of reservoir formation for Participant CAP336.

Approximately Maximum-Likelihood trees were used for each of the gene regions. OGV sequences are shown in magenta and proviral sequences are shown in black (non-hypermutated viral DNA) and gray (hypermutated viral DNA). Sequences generated from plasma collected within the first year of diagnosis are shown in shades of red, within the last year before therapy initiation are shown in shades of blue, with times between the first and last year shown as orange, yellow, and green. (TIF)</p

Timing of reservoir formation for Participant CAP372.

Approximately Maximum-Likelihood trees were used for each of the gene regions. OGV sequences are shown in magenta and proviral sequences are shown in black (non-hypermutated viral DNA) and gray (hypermutated viral DNA). Sequences generated from plasma collected within the first year of diagnosis are shown in shades of red, within the last year before therapy initiation are shown in shades of blue, with times between the first and last year shown as orange, yellow, and green. (TIF)</p

The Distribution of the Times Viral Sequences Entered the Pool of Long-Lived Cells Across the Cohort.

A. Each bar graph represents the distribution of entry into the pool of long-lived cells for an individual participant; the analysis pooled all unique sequences from OGVs, non-hypermutated proviral DNA, and hypermutated proviral DNA (masked). The total number of viral sequences analyzed is shown below each graph, along with the percent of sequences that cluster with variants circulating in the year before ART (percent late). The color scheme is the same as in Fig 1B with blue shades representing the year just before the initiation of therapy, red representing the first year after diagnosis of infection, and orange, yellow, and green representing intervening years. A black line is included to indicate the break between the late viruses (last year before therapy) and the earlier viruses. The bar graphs are ordered from the highest percentage of late sequences to the lowest percentage. B. The fraction of the viral sequences (estimated from both OGV and proviral DNA sequences) that entered the pool of long-lived cells each year for each participant is shown, where each line is for an individual participant. The fraction that entered during the first year after transmission is shown on the left, the fraction that entered in the last year prior to the initiation of therapy is shown on the right, and the point in between represents the fraction that entered on a per year basis between the first year of infection and the last year before therapy. The average value for each of these time frames is shown at the top of the graph (7% in the first year, 61% in the year just before therapy initiation, and 13% per year between the first and last years prior to therapy). The bars at the top indicate comparisons with * representing a P value of <0.05 and *** representing a P value of <0.001 as assessed using a Wilcoxon Matched Pairs Rank Sum Test.</p

Timing of reservoir formation for Participant CAP244.

Approximately Maximum-Likelihood trees were used for each of the gene regions. OGV sequences are shown in magenta and proviral sequences are shown in black (non-hypermutated viral DNA) and gray (hypermutated viral DNA). Sequences generated from plasma collected within the first year of diagnosis are shown in shades of red, within the last year before therapy initiation are shown in shades of blue, with times between the first and last year shown as orange, yellow, and green. (TIF)</p

Analysis of amplicon sequence quality for estimating time since infection.

Thirteen amplicons distributed across the HIV-1 genome were used to analyze RNA genomes isolated from the plasma of 18 participants at an average of 10 pre-ART timepoints. For each amplicon the average pairwise distance (PWD) was calculated between sequences at that timepoint and a consensus from the first timepoint. This was repeated at each timepoint during untreated infection and the relationship between change in PWD and weeks between timepoints was analyzed. For each amplicon, we show the average increase in pairwise distance per year and the correlation between change in PWD and time. (TIF)</p