Link between the numbers of particles and variants founding new HIV-1 infections depends on the timing of transmission

Reconciling founder variant multiplicity of HIV-1 infection with the rate of CD4+ decline

HIV-1 transmission precipitates a stringent genetic bottleneck, with 75% of new infections initiated by a single genetic variant. Where multiple variants initiate infection, recipient set point viral load (SpVL) and the rate of CD4+ T cell decline may be elevated, but these findings remain inconsistent. Here, we summarised the evidence for this phenomenon, then tested whether previous studies possessed sufficient statistical power to reliably identify a true effect of multiple variant infection leading to higher SpVL. Next, we combined models of HIV-1 transmission, heritability and disease progression to understand whether available data suggest a faster CD4+ T cell decline would be expected to associated with multiple variant infection, without an explicit dependency between the two. First, we found that most studies had insufficient power to identify a true significant difference, prompting an explanation for previous inconsistencies. Next, our model framework revealed we would not expect to observe a positive association between multiple variant infections and faster CD4+ T cell decline, in the absence of an explicit dependency. Consequently, while empirical evidence may be consistent with a positive association between multiple variant infection and faster CD4+ T cell decline, further investigation is required to establish a causal basis.

Inferring the multiplicity of founder variants initiating HIV-1 infection: a systematic review and individual patient data meta-analysis

BACKGROUND

HIV-1 infections initiated by multiple founder variants are characterised by a higher viral load and a worse clinical prognosis than those initiated with single founder variants, yet little is known about the routes of exposure through which transmission of multiple founder variants is most probable. Here we used individual patient data to calculate the probability of multiple founders stratified by route of HIV exposure and study methodology.

METHODS

We conducted a systematic review and meta-analysis of studies that estimated founder variant multiplicity in HIV-1 infection, searching MEDLINE, Embase, and Global Health databases for papers published between Jan 1, 1990, and Sept 14, 2020. Eligible studies must have reported original estimates of founder variant multiplicity in people with acute or early HIV-1 infections, have clearly detailed the methods used, and reported the route of exposure. Studies were excluded if they reported data concerning people living with HIV-1 who had known or suspected superinfection, who were documented as having received pre-exposure prophylaxis, or if the transmitting partner was known to be receiving antiretroviral treatment. Individual patient data were collated from all studies, with authors contacted if these data were not publicly available. We applied logistic meta-regression to these data 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 exposure routes in a univariable analysis. We then extended our model to adjust for different study methods in a multivariable analysis, recalculating estimates across the exposure routes. This study is registered with PROSPERO, CRD42020202672.

FINDINGS

We included 70 publications in our analysis, comprising 1657 individual patients. Our pooled estimate of the probability that an infection is initiated by multiple founder variants was 0·25 (95% CI 0·21-0·29), with moderate heterogeneity (Q=132·3, p<0·0001, I²=64·2%). Our multivariable analysis uncovered differences in the probability of multiple variant infection by exposure route. Relative to a baseline of male-to-female transmission, the predicted probability for female-to-male multiple variant transmission was significantly lower at 0·13 (95% CI 0·08-0·20), and the probabilities were significantly higher for transmissions in people who inject drugs (0·37 [0·24-0·53]) and men who have sex with men (0·30 [0·33-0·40]). There was no significant difference in the probability of multiple variant transmission between male-to-female transmission (0·21 [0·14-0·31]), post-partum transmission (0·18 [0·03-0·57]), pre-partum transmission (0·17 [0·08-0·33]), and intra-partum transmission (0·27 [0·14-0·45]).

INTERPRETATION

We identified that transmissions in people who inject drugs and men who have sex with men are significantly more likely to result in an infection initiated by multiple founder variants, and female-to-male infections are significantly less probable. Quantifying how the routes of HIV infection affect the transmission of multiple variants allows us to better understand how the evolution and epidemiology of HIV-1 determine clinical outcomes.

FUNDING

Medical Research Council Precision Medicine Doctoral Training Programme and a European Research Council Starting Grant.

The interplay between replication capacity of HIV-1 and surrogate markers of disease

BACKGROUND

HIV-1 replication capacity (RC) of transmitted/founder viruses may influence the further course of HIV-1 infection.

METHODS

Replication capacities (RCs) of 355 whole genome primary HIV-1 isolates derived from samples acquired during acute and recent primary HIV-1 infection (PHI) were determined using a novel high throughput infection assay in primary cells. The RCs were used to elucidate potential factors that could be associated with RC during PHI.

RESULTS

Increased RC was found to be associated with increased set point viral load (VL), and significant differences in RCs among 13 different HIV-1 subtypes were discerned. Notably, we observed an increase in RCs for primary HIV-1 isolates of HIV-1 subtype B over a 17-year period. Associations were not observed between RC and CD4 count at sample date of RC measurement, CD4 recovery after initiation of antiretroviral treatment (ART), CD4 decline in untreated individuals, and acute retroviral syndrome severity scores.

DISCUSSION

These findings highlight that RCs of primary HIV-1 isolates acquired during the acute and recent phase of infection are more associated with viral factors, i.e., set point VL, than with host factors. Furthermore, we observed a temporal increase in RC for HIV-1 subtype B viruses over a period of 17 years.

Early-Transmitted Variants and Their Evolution in a HIV-1 Positive Couple: NGS and Phylogenetic Analyses

We had access to both components of a couple who became infected with human immunodeficiency virus (HIV)-1 through sexual behavior during the early initial phase of infection and before initiation of therapy. We analyzed blood samples obtained at the time of diagnosis and after six months of combined antiretroviral therapy. Next-generation sequencing (NGS) and phylogenetic analyses were used to investigate the transmission and evolution of HIV-1 quasispecies. Phylogenetic analyses were conducted using Bayesian inference methods. Both partners were infected with an HIV-1 B subtype. No evidence of viral recombination was observed. The lowest intrapersonal genetic distances were observed at baseline, before initiation of therapy, and in particular in the V1V2 fragment (distances ranging from 0.102 to 0.148). One HIV-1 single variant was concluded to be dominant in all of the HIV-1 regions analyzed, although some minor variants could be observed. The same tree structure was observed both at baseline and after six months of therapy. These are the first extended phylogenetic analyses performed on both members of a therapy-naïve couple within a few weeks of infection, and in which the effect of antiretroviral therapy on viral evolution was analyzed. Understanding which HIV-1 variants are most likely to be transmitted would allow a better understanding of viral evolution, possibly playing a role in vaccine design and prevention strategies.

Infection with multiple HIV-1 founder variants is associated with lower viral replicative capacity, faster CD4+ T cell decline and increased immune activation during acute infection

HIV-1 transmission is associated with a severe bottleneck in which a limited number of variants from a pool of genetically diverse quasispecies establishes infection. The IAVI protocol C cohort of discordant couples, female sex workers, other heterosexuals and men who have sex with men (MSM) present varying risks of HIV infection, diverse HIV-1 subtypes and represent a unique opportunity to characterize transmitted/founder viruses (TF) where disease outcome is known. To identify the TF, the HIV-1 repertoire of 38 MSM participants' samples was sequenced close to transmission (median 21 days post infection, IQR 18-41) and assessment of multivariant infection done. Patient derived gag genes were cloned into an NL4.3 provirus to generate chimeric viruses which were characterized for replicative capacity (RC). Finally, an evaluation of how the TF virus predicted disease progression and modified the immune response at both acute and chronic HIV-1 infection was done. There was higher prevalence of multivariant infection compared with previously described heterosexual cohorts. A link was identified between multivariant infection and replicative capacity conferred by gag, whereby TF gag tended to be of lower replicative capacity in multivariant infection (p = 0.02) suggesting an overall lowering of fitness requirements during infection with multiple variants. Notwithstanding, multivariant infection was associated with rapid CD4+ T cell decline and perturbances in the CD4+ T cell and B cell compartments compared to single variant infection, which were reversible upon control of viremia. Strategies aimed at identifying and mitigating multivariant infection could contribute toward improving HIV-1 prognosis and this may involve strategies that tighten the stringency of the transmission bottleneck such as treatment of STI. Furthermore, the sequences and chimeric viruses help with TF based experimental vaccine immunogen design and can be used in functional assays to probe effective immune responses against TF.

Number of HIV-1 founder variants is determined by the recency of the source partner infection

During sexual transmission, the high genetic diversity of HIV-1 within an individual is frequently reduced to one founder variant that initiates infection. Understanding the drivers of this bottleneck is crucial to developing effective infection control strategies. Little is known about the importance of the source partner during this bottleneck. To test the hypothesis that the source partner affects the number of HIV founder variants, we developed a phylodynamic model calibrated using genetic and epidemiological data on all existing transmission pairs for whom the direction of transmission and the infection stage of the source partner are known. Our results suggest that acquiring infection from someone in the acute (early) stage of infection increases the risk of multiple-founder variant transmission compared with acquiring infection from someone in the chronic (later) stage of infection. This study provides the first direct test of source partner characteristics to explain the low frequency of multiple-founder strain infections.

A theoretical framework for transitioning from patient-level to population-scale epidemiological dynamics: influenza A as a case study

Multi-scale epidemic forecasting models have been used to inform population-scale predictions with within-host models and/or infection data collected in longitudinal cohort studies. However, most multi-scale models are complex and require significant modelling expertise to run. We formulate an alternative multi-scale modelling framework using a compartmental model with multiple infected stages. In the large-compartment limit, our easy-to-use framework generates identical results compared to previous more complicated approaches. We apply our framework to the case study of influenza A in humans. By using a viral dynamics model to generate synthetic patient-level data, we explore the effects of limited and inaccurate patient data on the accuracy of population-scale forecasts. If infection data are collected daily, we find that a cohort of at least 40 patients is required for a mean population-scale forecasting error below 10%. Forecasting errors may be reduced by including more patients in future cohort studies or by increasing the frequency of observations for each patient. Our work, therefore, provides not only an accessible epidemiological modelling framework but also an insight into the data required for accurate forecasting using multi-scale models.

Detection, forecasting and control of infectious disease epidemics: modelling outbreaks in humans, animals and plants

The 1918 influenza pandemic is one of the most devastating infectious disease epidemics on record, having caused approximately 50 million deaths worldwide. Control measures, including prohibiting non-essential gatherings as well as closing cinemas and music halls, were applied with varying success and limited knowledge of transmission dynamics. One hundred years later, following developments in the field of mathematical epidemiology, models are increasingly used to guide decision-making and devise appropriate interventions that mitigate the impacts of epidemics. Epidemiological models have been used as decision-making tools during outbreaks in human, animal and plant populations. However, as the subject has developed, human, animal and plant disease modelling have diverged. Approaches have been developed independently for pathogens of each host type, often despite similarities between the models used in these complementary fields. With the increased importance of a One Health approach that unifies human, animal and plant health, we argue that more inter-disciplinary collaboration would enhance each of the related disciplines. This pair of theme issues presents research articles written by human, animal and plant disease modellers. In this introductory article, we compare the questions pertinent to, and approaches used by, epidemiological modellers of human, animal and plant pathogens, and summarize the articles in these theme issues. We encourage future collaboration that transcends disciplinary boundaries and links the closely related areas of human, animal and plant disease epidemic modelling. This article is part of the theme issue 'Modelling infectious disease outbreaks in humans, animals and plants: approaches and important themes'. This issue is linked with the subsequent theme issue 'Modelling infectious disease outbreaks in humans, animals and plants: epidemic forecasting and control'.

Evolutionary consequences of feedbacks between within-host competition and disease control

Lay Summary: Competition often occurs among diverse parasites within a single host, but control efforts could change its strength. We examined how the interplay between competition and control could shape the evolution of parasite traits like drug resistance and disease severity.

Coinfections by noninteracting pathogens are not independent and require new tests of interaction

If pathogen species, strains, or clones do not interact, intuition suggests the proportion of coinfected hosts should be the product of the individual prevalences. Independence consequently underpins the wide range of methods for detecting pathogen interactions from cross-sectional survey data. However, the very simplest of epidemiological models challenge the underlying assumption of statistical independence. Even if pathogens do not interact, death of coinfected hosts causes net prevalences of individual pathogens to decrease simultaneously. The induced positive correlation between prevalences means the proportion of coinfected hosts is expected to be higher than multiplication would suggest. By modelling the dynamics of multiple noninteracting pathogens causing chronic infections, we develop a pair of novel tests of interaction that properly account for nonindependence between pathogens causing lifelong infection. Our tests allow us to reinterpret data from previous studies including pathogens of humans, plants, and animals. Our work demonstrates how methods to identify interactions between pathogens can be updated using simple epidemic models.

If pathogen species, strains, or clones do not interact, intuition suggests the proportion of coinfected hosts can be obtained by simply multiplying the individual prevalences. However, even simple epidemiological models show this to be untrue. This study develops new tests for interaction between pathogens that account for this surprising lack of statistical independence.