The contribution of viral genotype to plasma viral set-point in HIV infection

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.

A large population sample of African HIV genomes from the 1980s reveals a reduction in subtype D over time associated with propensity for CXCR4 tropism

We present 109 near full-length HIV genomes amplified from blood serum samples obtained during early 1986 from across Uganda, which to our knowledge is the earliest and largest population sample from the initial phase of the HIV epidemic in Africa. Consensus sequences were made from paired-end Illumina reads with a target-capture approach to amplify HIV material following poor success with standard approaches. In comparisons with a smaller 'intermediate' genome dataset from 1998 to 1999 and a 'modern' genome dataset from 2007 to 2016, the proportion of subtype D was significantly higher initially, dropping from 67% (73/109), to 57% (26/46) to 17% (82/465) respectively (p < 0.0001). Subtype D has previously been shown to have a faster rate of disease progression than other subtypes in East African population studies, and to have a higher propensity to use the CXCR4 co-receptor ("X4 tropism"); associated with a decrease in time to AIDS. Here we find significant differences in predicted tropism between A1 and D subtypes in all three sample periods considered, which is particularly striking the 1986 sample: 66% (53/80) of subtype D env sequences were predicted to be X4 tropic compared with none of the 24 subtype A1. We also analysed the frequency of subtype in the envelope region of inter-subtype recombinants, and found that subtype A1 is over-represented in env, suggesting recombination and selection have acted to remove subtype D env from circulation. The reduction of subtype D frequency over three decades therefore appears to be a result of selective pressure against X4 tropism and its higher virulence. Lastly, we find a subtype D specific codon deletion at position 24 of the V3 loop, which may explain the higher propensity for subtype D to utilise X4 tropism.

A phylogeny-aware GWAS framework to correct for heritable pathogen effects on infectious disease traits

Infectious diseases are particularly challenging for genome-wide association studies (GWAS) because genetic effects from two organisms (pathogen and host) can influence a trait. Traditional GWAS assume individual samples are independent observations. However, pathogen effects on a trait can be heritable from donor to recipient in transmission chains. Thus, residuals in GWAS association tests for host genetic effects may not be independent due to shared pathogen ancestry. We propose a new method to estimate and remove heritable pathogen effects on a trait based on the pathogen phylogeny prior to host GWAS, thus restoring independence of samples. In simulations, we show this additional step can increase GWAS power to detect truly associated host variants when pathogen effects are highly heritable, with strong phylogenetic correlations. We applied our framework to data from two different host-pathogen systems, HIV in humans and X. arboricola in A. thaliana. In both systems, the heritability and thus phylogenetic correlations turn out to be low enough such that qualitative results of GWAS do not change when accounting for the pathogen shared ancestry through a correction step. This means that previous GWAS results applied to these two systems should not be biased due to shared pathogen ancestry. In summary, our framework provides additional information on the evolutionary dynamics of traits in pathogen populations and may improve GWAS if pathogen effects are highly phylogenetically correlated amongst individuals in a cohort.

HIV-1 Entry and Prospects for Protecting against Infection

Human Immunodeficiency Virus type-1 (HIV-1) establishes a latent viral reservoir soon after infection, which poses a major challenge for drug treatment and curative strategies. Many efforts are therefore focused on blocking infection. To this end, both viral and host factors relevant to the onset of infection need to be considered. Given that HIV-1 is most often transmitted mucosally, strategies designed to protect against infection need to be effective at mucosal portals of entry. These strategies need to contend also with cell-free and cell-associated transmitted/founder (T/F) virus forms; both can initiate and establish infection. This review will discuss how insight from the current model of HIV-1 mucosal transmission and cell entry has highlighted challenges in developing effective strategies to prevent infection. First, we examine key viral and host factors that play a role in transmission and infection. We then discuss preventive strategies based on antibody-mediated protection, with emphasis on targeting T/F viruses and mucosal immunity. Lastly, we review treatment strategies targeting viral entry, with focus on the most clinically advanced entry inhibitors.

Heritability of the HIV-1 reservoir size and decay under long-term suppressive ART

The HIV-1 reservoir is the major hurdle to curing HIV-1. However, the impact of the viral genome on the HIV-1 reservoir, i.e. its heritability, remains unknown. We investigate the heritability of the HIV-1 reservoir size and its long-term decay by analyzing the distribution of those traits on viral phylogenies from both partial-pol and viral near full-length genome sequences. We use a unique nationwide cohort of 610 well-characterized HIV-1 subtype-B infected individuals on suppressive ART for a median of 5.4 years. We find that a moderate but significant fraction of the HIV-1 reservoir size 1.5 years after the initiation of ART is explained by genetic factors. At the same time, we find more tentative evidence for the heritability of the long-term HIV-1 reservoir decay. Our findings indicate that viral genetic factors contribute to the HIV-1 reservoir size and hence the infecting HIV-1 strain may affect individual patients’ hurdle towards a cure.

The HIV reservoir is a major hurdle for a cure of HIV, but the factors determining its size and dynamics remain unclear. Here the authors show in a large cohort of 610 HIV-1 infected individuals, who are on suppressive ART for a median of 5.4 years, that viral genetic factors contribute substantially to the HIV-1 reservoir size.

Inferring Phenotypic Trait Evolution on Large Trees With Many Incomplete Measurements

Comparative biologists are often interested in inferring covariation between multiple biological traits sampled across numerous related taxa. To properly study these relationships, we must control for the shared evolutionary history of the taxa to avoid spurious inference. An additional challenge arises as obtaining a full suite of measurements becomes increasingly difficult with increasing taxa. This generally necessitates data imputation or integration, and existing control techniques typically scale poorly as the number of taxa increases. We propose an inference technique that integrates out missing measurements analytically and scales linearly with the number of taxa by using a post-order traversal algorithm under a multivariate Brownian diffusion (MBD) model to characterize trait evolution. We further exploit this technique to extend the MBD model to account for sampling error or non-heritable residual variance. We test these methods to examine mammalian life history traits, prokaryotic genomic and phenotypic traits, and HIV infection traits. We find computational efficiency increases that top two orders-of-magnitude over current best practices. While we focus on the utility of this algorithm in phylogenetic comparative methods, our approach generalizes to solve long-standing challenges in computing the likelihood for matrix-normal and multivariate normal distributions with missing data at scale.

Proliferation and benevolence-A framework for dissecting the mechanisms of microbial virulence and health promotion

Key topics in the study of host–microbe interactions—such as the prevention of drug resistance and the exploitation of beneficial effects of bacteria—would benefit from concerted efforts with both mechanistic and evolutionary approaches. But due to differences in intellectual traditions, insights gained in one field rarely benefit the other. Here, we develop a conceptual and analytical framework for the integrated study of host–microbe interactions. This framework partitions the health effects of microbes and the effector molecules they produce into components with different evolutionary implications. It thereby facilitates the prediction of evolutionary responses to inhibition and exploitation of specific molecular mechanisms.

The Mtb-HIV syndemic interaction: why treating M. tuberculosis infection may be crucial for HIV-1 eradication

Accelerated tuberculosis and AIDS progression seen in HIV-1 and Mycobacterium tuberculosis (Mtb)-coinfected individuals indicates the important interaction between these syndemic pathogens. The immunological interaction between HIV-1 and Mtb has been largely defined by how the virus exacerbates tuberculosis disease pathogenesis. Understanding of the mechanisms by which pre-existing or subsequent Mtb infection may favor the replication, persistence and progression of HIV, is less characterized. We present a rationale for the critical consideration of ‘latent’ Mtb infection in HIV-1 prevention and cure strategies. In support of this position, we review evidence of the effect of Mtb infection on HIV-1 acquisition, replication and persistence. We propose that ‘latent’ Mtb infection may have considerable impact on HIV-1 pathogenesis and the continuing HIV-1 epidemic in sub-Saharan Africa.

Natural selection favoring more transmissible HIV detected in United States molecular transmission network

HIV molecular epidemiology can identify clusters of individuals with elevated rates of HIV transmission. These variable transmission rates are primarily driven by host risk behavior; however, the effect of viral traits on variable transmission rates is poorly understood. Viral load, the concentration of HIV in blood, is a heritable viral trait that influences HIV infectiousness and disease progression. Here, we reconstruct HIV genetic transmission clusters using data from the United States National HIV Surveillance System and report that viruses in clusters, inferred to be frequently transmitted, have higher viral loads at diagnosis. Further, viral load is higher in people in larger clusters and with increased network connectivity, suggesting that HIV in the United States is experiencing natural selection to be more infectious and virulent. We also observe a concurrent increase in viral load at diagnosis over the last decade. This evolutionary trajectory may be slowed by prevention strategies prioritized toward rapidly growing transmission clusters.

Fast likelihood calculation for multivariate Gaussian phylogenetic models with shifts

Phylogenetic comparative methods (PCMs) have been used to study the evolution of quantitative traits in various groups of organisms, ranging from micro-organisms to animal and plant species. A common approach has been to assume a Gaussian phylogenetic model for the trait evolution along the tree, such as a branching Brownian motion (BM) or an Ornstein-Uhlenbeck (OU) process. Then, the parameters of the process have been inferred based on a given tree and trait data for the sampled species. At the heart of this inference lie multiple calculations of the model likelihood, that is, the probability density of the observed trait data, conditional on the model parameters and the tree. With the increasing availability of big phylogenetic trees, spanning hundreds to several thousand sampled species, this approach is facing a two-fold challenge. First, the assumption of a single Gaussian process governing the entire tree is not adequate in the presence of heterogeneous evolutionary forces acting in different parts of the tree. Second, big trees present a computational challenge, due to the time and memory complexity of the model likelihood calculation. Here, we explore a sub-family, denoted G_LInv, of the Gaussian phylogenetic models, with the transition density exhibiting the properties that the expectation depends Linearly on the ancestral trait value and the variance is Invariant with respect to the ancestral value. We show that G_LInv contains the vast majority of Gaussian models currently used in PCMs, while supporting an efficient (linear in the number of nodes) algorithm for the likelihood calculation. The algorithm supports scenarios with missing data, as well as different types of trees, including trees with polytomies and non-ultrametric trees. To account for the heterogeneity in the evolutionary forces, the algorithm supports models with "shifts" occurring at specific points in the tree. Such shifts can include changes in some or all parameters, as well as the type of the model, provided that the model remains within the G_LInv family. This contrasts with most of the current implementations where, due to slow likelihood calculation, the shifts are restricted to specific parameters in a single type of model, such as the long-term selection optima of an OU process, assuming that all of its other parameters, such as evolutionary rate and selection strength, are global for the entire tree. We provide an implementation of this likelihood calculation algorithm in an accompanying R-package called PCMBase. The package has been designed as a generic library that can be integrated with existing or novel maximum likelihood or Bayesian inference tools.

Automatic generation of evolutionary hypotheses using mixed Gaussian phylogenetic models

Phylogenetic comparative methods are widely used to understand and quantify the evolution of phenotypic traits, based on phylogenetic trees and trait measurements of extant species. Such analyses depend crucially on the underlying model. Gaussian phylogenetic models like Brownian motion and Ornstein-Uhlenbeck processes are the workhorses of modeling continuous-trait evolution. However, these models fit poorly to big trees, because they neglect the heterogeneity of the evolutionary process in different lineages of the tree. Previous works have addressed this issue by introducing shifts in the evolutionary model occurring at inferred points in the tree. However, for computational reasons, in all current implementations, these shifts are "intramodel," meaning that they allow jumps in 1 or 2 model parameters, keeping all other parameters "global" for the entire tree. There is no biological reason to restrict a shift to a single model parameter or, even, to a single type of model. Mixed Gaussian phylogenetic models (MGPMs) incorporate the idea of jointly inferring different types of Gaussian models associated with different parts of the tree. Here, we propose an approximate maximum-likelihood method for fitting MGPMs to comparative data comprising possibly incomplete measurements for several traits from extant and extinct phylogenetically linked species. We applied the method to the largest published tree of mammal species with body- and brain-mass measurements, showing strong statistical support for an MGPM with 12 distinct evolutionary regimes. Based on this result, we state a hypothesis for the evolution of the brain-body-mass allometry over the past 160 million y.

Dissecting HIV Virulence: Heritability of Setpoint Viral Load, CD4+ T-Cell Decline, and Per-Parasite Pathogenicity

Pathogen strains may differ in virulence because they attain different loads in their hosts, or because they induce different disease-causing mechanisms independent of their load. In evolutionary ecology, the latter is referred to as “per-parasite pathogenicity”. Using viral load and CD4+ T-cell measures from 2014 HIV-1 subtype B-infected individuals enrolled in the Swiss HIV Cohort Study, we investigated if virulence—measured as the rate of decline of CD4+ T cells—and per-parasite pathogenicity are heritable from donor to recipient. We estimated heritability by donor–recipient regressions applied to 196 previously identified transmission pairs, and by phylogenetic mixed models applied to a phylogenetic tree inferred from HIV pol sequences. Regressing the CD4+ T-cell declines and per-parasite pathogenicities of the transmission pairs did not yield heritability estimates significantly different from zero. With the phylogenetic mixed model, however, our best estimate for the heritability of the CD4+ T-cell decline is 17% (5–30%), and that of the per-parasite pathogenicity is 17% (4–29%). Further, we confirm that the set-point viral load is heritable, and estimate a heritability of 29% (12–46%). Interestingly, the pattern of evolution of all these traits differs significantly from neutrality, and is most consistent with stabilizing selection for the set-point viral load, and with directional selection for the CD4+ T-cell decline and the per-parasite pathogenicity. Our analysis shows that the viral genotype affects virulence mainly by modulating the per-parasite pathogenicity, while the indirect effect via the set-point viral load is minor.

Interaction of the Host and Viral Genome and Their Influence on HIV Disease

The course of Human Immunodeficiency Virus type 1 (HIV) infection is a dynamic interplay in which both host and viral genetic variation, among other factors, influence disease susceptibility and rate of progression. HIV set-point viral load (spVL), a key indicator of HIV disease progression, has an estimated 30% of variance attributable to common heritable effects and roughly 70% attributable to environmental factors and/or additional non-genetic factors. Genome-wide genotyping and sequencing studies have allowed for large-scale association testing studying host and viral genetic variants associated with infection and disease progression. Host genomics of HIV infection has been studied predominantly in Caucasian populations consistently identifying human leukocyte antigen (HLA) genes and C-C motif chemokine receptor 5 as key factors of HIV susceptibility and progression. However, these studies don’t fully assess all classes of genetic variation (e.g., very rare polymorphisms, copy number variants etc.) and do not inform on non-European ancestry groups. Additionally, viral sequence variability has been demonstrated to influence disease progression independently of host genetic variation. Viral sequence variation can be attributed to the rapid evolution of the virus within the host due to the selective pressure of the host immune response. As the host immune system responds to the virus, e.g., through recognition of HIV antigens, the virus is able to mitigate this response by evolving HLA-specific escape mutations. Diversity of viral genotypes has also been correlated with moderate to strong effects on CD4+ T cell decline and some studies showing weak to no correlation with spVL. There is evidence to support these viral genetic factors being heritable between individuals and the evolution of these factors having important consequences in the genetic epidemiology of HIV infection on a population level. This review will discuss the host-pathogen interaction of HIV infection, explore the importance of host and viral genetics for a better understanding of pathogenesis and identify opportunities for additional genetic studies.

Circumcision status at HIV infection is not associated with plasma viral load in men: analysis of specimens from a randomized controlled trial

BACKGROUND

Male circumcision provides men with approximately 60% protection from acquiring HIV infection via heterosexual sex, and has become a key component of HIV prevention efforts in sub-Saharan Africa. Possible mechanisms for this protection include removal of the inflammatory anaerobic sub-preputial environment and the high concentration of Langerhans cells on the inside of the foreskin, both believed to promote local vulnerability to HIV infection. In people who do acquire HIV, viral load is partially determined by infecting partner viral load, potentially mediated by size of infecting inoculum. By removing a portal for virion entry, prior male circumcision could decrease infecting inoculum and thus viral load in men who become HIV-infected, conferring the known associated benefits of slower progression to disease and decreased infectiousness.

METHODS

We performed an as-treated analysis of plasma samples collected under a randomized controlled trial of male circumcision for HIV prevention, comparing men based on their circumcision status at the time of HIV acquisition, to determine whether circumcision is associated with lower viral load. Eligible men were seroconverters who had at least one plasma sample available drawn at least 6 months after infection, reported no potential exposures other than vaginal sex and, for those who were circumcised, were infected more than 6 weeks after circumcision, to eliminate the open wound as a confounder. Initial viral load testing indicated that quality of pre-2007 samples might have been compromised during storage and they were excluded, as were those with undetectable or unquantifiable results. Log viral loads were compared between groups using univariable and multivariable linear regression, adjusting for sample age and sexually transmitted infection diagnosis with 3.5 months of seroconversion, with a random effect for intra-individual clustering for samples from the same man. A per-protocol analysis was also performed.

RESULTS

There were no viral load differences between men who were circumcised and uncircumcised at the time of HIV infection (means 4.00 and 4.03 log₁₀ copies/mL respectively, p = .88) in any analysis.

CONCLUSION

Circumcision status at the time of HIV infection does not affect viral load in men.

TRIAL REGISTRATION

The original RCT which provided the samples was ClinicalTrials.gov trial NCT00059371 .

A Practical Guide to Estimating the Heritability of Pathogen Traits

Pathogen traits, such as the virulence of an infection, can vary significantly between patients. A major challenge is to measure the extent to which genetic differences between infecting strains explain the observed variation of the trait. This is quantified by the trait’s broad-sense heritability, H². A recent discrepancy between estimates of the heritability of HIV-virulence has opened a debate on the estimators’ accuracy. Here, we show that the discrepancy originates from model limitations and important lifecycle differences between sexually reproducing organisms and transmittable pathogens. In particular, current quantitative genetics methods, such as donor–recipient regression of surveyed serodiscordant couples and the phylogenetic mixed model (PMM), are prone to underestimate H², because they neglect or do not fit to the loss of resemblance between transmission partners caused by within-host evolution. In a phylogenetic analysis of 8,483 HIV patients from the United Kingdom, we show that the phenotypic correlation between transmission partners decays with the amount of within-host evolution of the virus. We reproduce this pattern in toy-model simulations and show that a phylogenetic Ornstein–Uhlenbeck model (POUMM) outperforms the PMM in capturing this correlation pattern and in quantifying H². In particular, we show that POUMM outperforms PMM even in simulations without selection—as it captures the mentioned correlation pattern—which has not been appreciated until now. By cross-validating the POUMM estimates with ANOVA on closest phylogenetic pairs, we obtain H² ≈ 0.2, meaning ∼20% of the variation in HIV-virulence is explained by the virus genome both for European and African data.

The impact of HIV-1 within-host evolution on transmission dynamics

The adaptive potential of HIV-1 is a vital mechanism to evade host immune responses and antiviral treatment. However, high evolutionary rates during persistent infection can impair transmission efficiency and alter disease progression in the new host, resulting in a delicate trade-off between within-host virulence and between-host infectiousness. This trade-off is visible in the disparity in evolutionary rates at within-host and between-host levels, and preferential transmission of ancestral donor viruses. Understanding the impact of within-host evolution for epidemiological studies is essential for the design of preventive and therapeutic measures. Herein, we review recent theoretical and experimental work that generated new insights into the complex link between within-host evolution and between-host fitness, revealing temporal and selective processes underlying the structure and dynamics of HIV-1 transmission.

Gut transcriptome analysis on females of Ornithodoros mimon (Acari: Argasidae) and phylogenetic inference of ticks

Abstract Ornithodoros mimon is an argasid tick that parasitizes bats, birds and opossums and is also harmful to humans. Knowledge of the transcripts present in the tick gut helps in understanding the role of vital molecules in the digestion process and parasite-host relationship, while also providing information about the evolution of arthropod hematophagy. Thus, the present study aimed to know and ascertain the main molecules expressed in the gut of argasid after their blood meal, through analysis on the gut transcriptome of engorged females of O. mimon using 454-based RNA sequencing. The gut transcriptome analysis reveals several transcripts associated with hemoglobin digestion, such as serine, cysteine, aspartic proteases and metalloenzymes. The phylogenetic analysis on the peptidases confirmed that most of them are clustered with other tick genes. We recorded the presence a cathepsin O peptidase-coding transcript in ticks. The topology of the phylogenetic inferences, based on transcripts of inferred families of homologues, was similar to that of previous reports based on mitochondrial genome and nuclear rRNA sequences. We deposited 2,213 sequence of O. mimon to the public databases. Our findings may help towards better understanding of important argasid metabolic processes, such as digestion, nutrition and immunity.

Viral genetic variation accounts for a third of variability in HIV-1 set-point viral load in Europe

HIV-1 set-point viral load-the approximately stable value of viraemia in the first years of chronic infection-is a strong predictor of clinical outcome and is highly variable across infected individuals. To better understand HIV-1 pathogenesis and the evolution of the viral population, we must quantify the heritability of set-point viral load, which is the fraction of variation in this phenotype attributable to viral genetic variation. However, current estimates of heritability vary widely, from 6% to 59%. Here we used a dataset of 2,028 seroconverters infected between 1985 and 2013 from 5 European countries (Belgium, Switzerland, France, the Netherlands and the United Kingdom) and estimated the heritability of set-point viral load at 31% (CI 15%-43%). Specifically, heritability was measured using models of character evolution describing how viral load evolves on the phylogeny of whole-genome viral sequences. In contrast to previous studies, (i) we measured viral loads using standardized assays on a sample collected in a strict time window of 6 to 24 months after infection, from which the viral genome was also sequenced; (ii) we compared 2 models of character evolution, the classical "Brownian motion" model and another model ("Ornstein-Uhlenbeck") that includes stabilising selection on viral load; (iii) we controlled for covariates, including age and sex, which may inflate estimates of heritability; and (iv) we developed a goodness of fit test based on the correlation of viral loads in cherries of the phylogenetic tree, showing that both models of character evolution fit the data well. An overall heritability of 31% (CI 15%-43%) is consistent with other studies based on regression of viral load in donor-recipient pairs. Thus, about a third of variation in HIV-1 virulence is attributable to viral genetic variation.

Parent-offspring regression to estimate the heritability of an HIV-1 trait in a realistic setup

Background

Parent-offspring (PO) regression is a central tool to determine the heritability of phenotypic traits; i.e., the relative extent to which those traits are controlled by genetic factors. The applicability of PO regression to viral traits is unclear because the direction of viral transmission—who is the donor (parent) and who is the recipient (offspring)—is typically unknown and viral phylogenies are sparsely sampled.

Methods

We assessed the applicability of PO regression in a realistic setting using Ornstein–Uhlenbeck simulated data on phylogenies built from 11,442 Swiss HIV Cohort Study (SHCS) partial pol sequences and set-point viral load (SPVL) data from 3293 patients.

Results

We found that the misidentification of donor and recipient plays a minor role in estimating heritability and showed that sparse sampling does not influence the mean heritability estimated by PO regression. A mixed-effect model approach yielded the same heritability as PO regression but could be extended to clusters of size greater than 2 and allowed for the correction of confounding effects. Finally, we used both methods to estimate SPVL heritability in the SHCS. We employed a wide range of transmission pair criteria to measure heritability and found a strong dependence of the heritability estimates to these criteria. For the most conservative genetic distance criteria, for which heritability estimates are conceptually expected to be closest to true heritability, we found estimates ranging from 32 to 46% across different bootstrap criteria. For less conservative distance criteria, we found estimates ranging down to 8%. All estimates did not change substantially after adjusting for host-demographic factors in the mixed-effect model (±2%).

Conclusions

For conservative transmission pair criteria, both PO regression and mixed-effect models are flexible and robust tools to estimate the contribution of viral genetic effects to viral traits under real-world settings. Overall, we find a strong effect of viral genetics on SPVL that is not confounded by host demographics.

Electronic supplementary material

The online version of this article (doi:10.1186/s12977-017-0356-3) contains supplementary material, which is available to authorized users.

Mining for pairs: shared clinic visit dates identify steady HIV-positive partnerships

OBJECTIVES

Here we examined the hypothesis that some stable HIV-infected partnerships can be found in cohort studies, as the patients frequently attend the clinic visits together.

METHODS

Using mathematical approximations and shuffling to derive the probabilities of sharing a given number of visits by chance, we identified and validated couples that may represent either transmission pairs or serosorting couples in a stable relationship.

RESULTS

We analysed 434 432 visits for 16 139 Swiss HIV Cohort Study patients from 1990 to 2014. For 89 pairs, the number of shared visits exceeded the number expected. Of these, 33 transmission pairs were confirmed on the basis of three criteria: an extensive phylogenetic tree, a self-reported steady HIV-positive partnership, and risk group affiliation. Notably, 12 of the validated transmission pairs (36%; 12 of 33) were of a mixed ethnicity with a large median age gap [17.5 years; interquartile range (IQR) 11.8-22 years] and these patients harboured HIV-1 of predominantly non-B subtypes, suggesting imported infections.

CONCLUSIONS

In the context of the surge in research interest in HIV transmission pairs, this simple method widens the horizons of research on within-pair quasi-species exchange, transmitted drug resistance and viral recombination at the biological level and targeted prevention at the public health level.

Estimating the Respective Contributions of Human and Viral Genetic Variation to HIV Control

We evaluated the fraction of variation in HIV-1 set point viral load attributable to viral or human genetic factors by using joint host/pathogen genetic data from 541 HIV infected individuals. We show that viral genetic diversity explains 29% of the variation in viral load while host factors explain 8.4%. Using a joint model including both host and viral effects, we estimate a total of 30% heritability, indicating that most of the host effects are reflected in viral sequence variation.

Viral loads of Human Immunodeficiency Virus infections are correlated between the donor and the recipient of the transmission pair. Similarly, human genetic factors may modulate viral load. In this study we estimate the extents to which viral load is heritable either via the viral genotype (from donor to recipient) or via the host’s Human Leukocyte Antigen (HLA) genotype. We find that a major fraction of inter individual variability is explained by the similarity of the viral genotypes, and that human genetic variation in the HLA region provide little additional explanatory power.

Emerging Concepts of Data Integration in Pathogen Phylodynamics

Phylodynamics has become an increasingly popular statistical framework to extract evolutionary and epidemiological information from pathogen genomes. By harnessing such information, epidemiologists aim to shed light on the spatio-temporal patterns of spread and to test hypotheses about the underlying interaction of evolutionary and ecological dynamics in pathogen populations. Although the field has witnessed a rich development of statistical inference tools with increasing levels of sophistication, these tools initially focused on sequences as their sole primary data source. Integrating various sources of information, however, promises to deliver more precise insights in infectious diseases and to increase opportunities for statistical hypothesis testing. Here, we review how the emerging concept of data integration is stimulating new advances in Bayesian evolutionary inference methodology which formalize a marriage of statistical thinking and evolutionary biology. These approaches include connecting sequence to trait evolution, such as for host, phenotypic and geographic sampling information, but also the incorporation of covariates of evolutionary and epidemic processes in the reconstruction procedures. We highlight how a full Bayesian approach to covariate modeling and testing can generate further insights into sequence evolution, trait evolution, and population dynamics in pathogen populations. Specific examples demonstrate how such approaches can be used to test the impact of host on rabies and HIV evolutionary rates, to identify the drivers of influenza dispersal as well as the determinants of rabies cross-species transmissions, and to quantify the evolutionary dynamics of influenza antigenicity. Finally, we briefly discuss how data integration is now also permeating through the inference of transmission dynamics, leading to novel insights into tree-generative processes and detailed reconstructions of transmission trees. [Bayesian inference; birth–death models; coalescent models; continuous trait evolution; covariates; data integration; discrete trait evolution; pathogen phylodynamics.

A transmission-virulence evolutionary trade-off explains attenuation of HIV-1 in Uganda

Evolutionary theory hypothesizes that intermediate virulence maximizes pathogen fitness as a result of a trade-off between virulence and transmission, but empirical evidence remains scarce. We bridge this gap using data from a large and long-standing HIV-1 prospective cohort, in Uganda. We use an epidemiological-evolutionary model parameterised with this data to derive evolutionary predictions based on analysis and detailed individual-based simulations. We robustly predict stabilising selection towards a low level of virulence, and rapid attenuation of the virus. Accordingly, set-point viral load, the most common measure of virulence, has declined in the last 20 years. Our model also predicts that subtype A is slowly outcompeting subtype D, with both subtypes becoming less virulent, as observed in the data. Reduction of set-point viral loads should have resulted in a 20% reduction in incidence, and a three years extension of untreated asymptomatic infection, increasing opportunities for timely treatment of infected individuals.

The impact of antiretroviral therapy on population-level virulence evolution of HIV-1

In HIV-infected patients, an individual's set point viral load (SPVL) strongly predicts disease progression. Some think that SPVL is evolving, indicating that the virulence of the virus may be changing, but the data are not consistent. In addition, the widespread use of antiretroviral therapy (ART) has the potential to drive virulence evolution. We develop a simple deterministic model designed to answer the following questions: what are the expected patterns of virulence change in the initial decades of an epidemic? Could administration of ART drive changes in virulence evolution and, what is the potential size and direction of this effect? We find that even without ART we would not expect monotonic changes in average virulence. Transient decreases in virulence following the peak of an epidemic are not necessarily indicative of eventual evolution to avirulence. In the short term, we would expect widespread ART to cause limited downward pressure on virulence. In the long term, the direction of the effect is determined by a threshold condition, which we define. We conclude that, given the surpassing benefits of ART to the individual and in reducing onward transmission, virulence evolution considerations need have little bearing on how we treat.

Potential Pitfalls in Estimating Viral Load Heritability

In HIV patients, the set-point viral load (SPVL) is the most widely used predictor of disease severity. Yet SPVL varies over several orders of magnitude between patients. The heritability of SPVL quantifies how much of the variation in SPVL is due to transmissible viral genetics. There is currently no clear consensus on the value of SPVL heritability, as multiple studies have reported apparently discrepant estimates. Here we illustrate that the discrepancies in estimates are most likely due to differences in the estimation methods, rather than the study populations. Importantly, phylogenetic estimates run the risk of being strongly confounded by unrealistic model assumptions. Care must be taken when interpreting and comparing the different estimates to each other.

HIV-1 infections with multiple founders are associated with higher viral loads than infections with single founders

Given the wide differences in HIV-1 viral load (VL) setpoint across subjects as opposed to fairly stable VL over time within an infected individual, it is important to identify host and viral characteristics that affect VL setpoint. While recently-infected individuals with multiple phylogenetically-linked HIV-1 founder variants represent a minority of HIV-1 infections, we found in two different cohorts that more diverse HIV-1 populations in early infection were associated with significantly higher VL one year after HIV-1 diagnosis.

Bottlenecks in HIV-1 transmission: insights from the study of founder viruses

HIV-1 infection typically results from the transmission of a single viral variant, the transmitted/founder (T/F) virus. Studies of these HIV-1 variants provide critical information about the transmission bottlenecks and the selective pressures acting on the virus in the transmission fluid and in the recipient tissues. These studies reveal that T/F virus phenotypes are shaped by stochastic and selective forces that restrict transmission and may be targets for prevention strategies. In this Review, we highlight how studies of T/F viruses contribute to a better understanding of the biology of HIV-1 transmission and discuss how these findings affect HIV-1 prevention strategies.

High heritability is compatible with the broad distribution of set point viral load in HIV carriers

Set point viral load in HIV patients ranges over several orders of magnitude and is a key determinant of disease progression in HIV. A number of recent studies have reported high heritability of set point viral load implying that viral genetic factors contribute substantially to the overall variation in viral load. The high heritability is surprising given the diversity of host factors associated with controlling viral infection. Here we develop an analytical model that describes the temporal changes of the distribution of set point viral load as a function of heritability. This model shows that high heritability is the most parsimonious explanation for the observed variance of set point viral load. Our results thus not only reinforce the credibility of previous estimates of heritability but also shed new light onto mechanisms of viral pathogenesis.

Following an initial peak in viremia, the viral load in HIV infected patients settles down to a set point which remains more or less stable during chronic HIV infection. This set point viral load is one of the key factors determining the rate of disease progression. The extent to which it is determined by the virus versus host genetics is thus central to developing a better understanding of disease progression. Here we develop an analytical model that describes the changes of the distribution of set point viral load in the HIV carrier population over a full cycle of transmission. Applying this model to patient data we find that the most parsimonious explanation for the observed large variation of set point viral load across HIV patients is that set point viral load is highly heritable from donors to recipients. This implies that set point viral load is to a considerable extent under the genetic control of the virus.

Simultaneously estimating evolutionary history and repeated traits phylogenetic signal: applications to viral and host phenotypic evolution

Phylogenetic signal quantifies the degree to which resemblance in continuously-valued traits reflects phylogenetic relatedness. Measures of phylogenetic signal are widely used in ecological and evolutionary research, and are recently gaining traction in viral evolutionary studies. Standard estimators of phylogenetic signal frequently condition on data summary statistics of the repeated trait observations and fixed phylogenetics trees, resulting in information loss and potential bias. To incorporate the observation process and phylogenetic uncertainty in a model-based approach, we develop a novel Bayesian inference method to simultaneously estimate the evolutionary history and phylogenetic signal from molecular sequence data and repeated multivariate traits. Our approach builds upon a phylogenetic diffusion framework that model continuous trait evolution as a Brownian motion process and incorporates Pagel's λ transformation parameter to estimate dependence among traits. We provide a computationally efficient inference implementation in the BEAST software package. We evaluate the synthetic performance of the Bayesian estimator of phylogenetic signal against standard estimators, and demonstrate the use of our coherent framework to address several virus-host evolutionary questions, including virulence heritability for HIV, antigenic evolution in influenza and HIV, and Drosophila sensitivity to sigma virus infection. Finally, we discuss model extensions that will make useful contributions to our flexible framework for simultaneously studying sequence and trait evolution.

Immuno-epidemiological modeling of HIV-1 predicts high heritability of the set-point virus load, while selection for CTL escape dominates virulence evolution

It has been suggested that HIV-1 has evolved its set-point virus load to be optimized for transmission. Previous epidemiological models and studies into the heritability of set-point virus load confirm that this mode of adaptation within the human population is feasible. However, during the many cycles of replication between infection of a host and transmission to the next host, HIV-1 is under selection for escape from immune responses, and not transmission. Here we investigate with computational and mathematical models how these two levels of selection, within-host and between-host, are intertwined. We find that when the rate of immune escape is comparable to what has been observed in patients, immune selection within hosts is dominant over selection for transmission. Surprisingly, we do find high values for set-point virus load heritability, and argue that high heritability estimates can be caused by the 'footprints' left by differing hosts' immune systems on the virus.

Differences in HIV type 1 neutralization breadth in 2 geographically distinct cohorts in Africa

To investigate whether distinct populations have differing human immunodeficiency virus type 1 (HIV) neutralizing antibody responses, we compared 20 women from Tanzania's HIV Superinfection Study (HISIS) cohort, who were infected multiple HIV subtypes, and 22 women from the Centre for the AIDS Programme of Research in South Africa (CAPRISA) cohort, who were infected exclusively with HIV subtype C. By 2 years after infection, 35% of HISIS subjects developed neutralization breadth, compared with 9% of CAPRISA subjects (P = .0131). Cumulative viral loads between 3 and 12 months were higher in the HISIS group (P = .046) and strongly associated with breadth (P < .0001). While viral load was the strongest predictor, other factors may play a role, as the odds of developing breadth remained higher in HISIS even after correction for viral load.