Phylogenetic patterns recover known HIV epidemiological relationships and reveal common transmission of multiple variants

HIV-1 transmitted drug resistance in newly diagnosed individuals in Italy over the period 2015-21

BACKGROUND

Transmitted drug resistance (TDR) is still a critical aspect for the management of individuals living with HIV-1. Thus, its evaluation is crucial to optimize HIV care.

METHODS

Overall, 2386 HIV-1 protease/reverse transcriptase and 1831 integrase sequences from drug-naïve individuals diagnosed in north and central Italy between 2015 and 2021 were analysed. TDR was evaluated over time. Phylogeny was generated by maximum likelihood. Factors associated with TDR were evaluated by logistic regression.

RESULTS

Individuals were mainly male (79.1%) and Italian (56.2%), with a median (IQR) age of 38 (30-48). Non-B infected individuals accounted for 44.6% (N = 1065) of the overall population and increased over time (2015-2021, from 42.1% to 51.0%, P = 0.002). TDR prevalence to any class was 8.0% (B subtype 9.5% versus non-B subtypes 6.1%, P = 0.002) and remained almost constant over time. Overall, 300 transmission clusters (TCs) involving 1155 (48.4%) individuals were identified, with a similar proportion in B and non-infected individuals (49.7% versus 46.8%, P = 0.148). A similar prevalence of TDR among individuals in TCs and those out of TCs was found (8.2% versus 7.8%, P = 0.707).By multivariable analysis, subtypes A, F, and CFR02_AG were negatively associated with TDR. No other factors, including being part of TCs, were significantly associated with TDR.

CONCLUSIONS

Between 2015 and 2021, TDR prevalence in Italy was 8% and remained almost stable over time. Resistant strains were found circulating regardless of being in TCs, but less likely in non-B subtypes. These results highlight the importance of a continuous surveillance of newly diagnosed individuals for evidence of TDR to inform clinical practice.

Higher HIV-1 evolutionary rate is associated with cytotoxic T lymphocyte escape mutations in infants

Escape from cytotoxic T lymphocyte (CTL) responses toward HIV-1 Gag and Nef has been associated with reduced control of HIV-1 replication in adults. However, less is known about CTL-driven immune selection in infants as longitudinal studies of infants are limited. Here, 1,210 gag and 1,264 nef sequences longitudinally collected within 15 months after birth from 14 HIV-1 perinatally infected infants and their mothers were analyzed. The number of transmitted founder (T/F) viruses and associations between virus evolution, selection, CTL escape, and disease progression were determined. The analyses indicated that a paraphyletic-monophyletic relationship between the mother-infant sequences was common (80%), and that the HIV-1 infection was established by a single T/F virus in 10 of the 12 analyzed infants (83%). Furthermore, most HIV-1 CTL escape mutations among infants were transmitted from the mothers and did not revert during the first year of infection. Still, immune-driven selection was observed at approximately 3 months after HIV-1 infection in infants. Moreover, virus populations with CTL escape mutations in gag evolved faster than those without, independently of disease progression rate. These findings expand the current knowledge of HIV-1 transmission, evolution, and CTL escape in infant HIV-1 infection and are relevant for the development of immune-directed interventions in infants.IMPORTANCEDespite increased coverage in antiretroviral therapy for the prevention of perinatal transmission, paediatric HIV-1 infection remains a significant public health concern, especially in areas of high HIV-1 prevalence. Understanding HIV-1 transmission and the subsequent virus adaptation from the mother to the infant's host environment, as well as the viral factors that affect disease outcome, is important for the development of early immune-directed interventions for infants. This study advances our understanding of vertical HIV-1 transmission, and how infant immune selection pressure is shaping the intra-host evolutionary dynamics of HIV-1.

Inferring HIV transmission patterns from viral deep-sequence data via latent typed point processes

Viral deep-sequencing data play a crucial role toward understanding disease transmission network flows, providing higher resolution compared to standard Sanger sequencing. To more fully utilize these rich data and account for the uncertainties in outcomes from phylogenetic analyses, we propose a spatial Poisson process model to uncover human immunodeficiency virus (HIV) transmission flow patterns at the population level. We represent pairings of individuals with viral sequence data as typed points, with coordinates representing covariates such as gender and age and point types representing the unobserved transmission statuses (linkage and direction). Points are associated with observed scores on the strength of evidence for each transmission status that are obtained through standard deep-sequence phylogenetic analysis. Our method is able to jointly infer the latent transmission statuses for all pairings and the transmission flow surface on the source-recipient covariate space. In contrast to existing methods, our framework does not require preclassification of the transmission statuses of data points, and instead learns them probabilistically through a fully Bayesian inference scheme. By directly modeling continuous spatial processes with smooth densities, our method enjoys significant computational advantages compared to previous methods that rely on discretization of the covariate space. We demonstrate that our framework can capture age structures in HIV transmission at high resolution, bringing valuable insights in a case study on viral deep-sequencing data from Southern Uganda.

Inferring Viral Transmission Time from Phylogenies for Known Transmission Pairs

When the time of an HIV transmission event is unknown, methods to identify it from virus genetic data can reveal the circumstances that enable transmission. We developed a single-parameter Markov model to infer transmission time from an HIV phylogeny constructed of multiple virus sequences from people in a transmission pair. Our method finds the statistical support for transmission occurring in different possible time slices. We compared our time-slice model results to previously described methods: a tree-based logical transmission interval, a simple parsimony-like rules-based method, and a more complex coalescent model. Across simulations with multiple transmitted lineages, different transmission times relative to the source's infection, and different sampling times relative to transmission, we found that overall our time-slice model provided accurate and narrower estimates of the time of transmission. We also identified situations when transmission time or direction was difficult to estimate by any method, particularly when transmission occurred long after the source was infected and when sampling occurred long after transmission. Applying our model to real HIV transmission pairs showed some agreement with facts known from the case investigations. We also found, however, that uncertainty on the inferred transmission time was driven more by uncertainty from time calibration of the phylogeny than from the model inference itself. Encouragingly, comparable performance of the Markov time-slice model and the coalescent model-which make use of different information within a tree-suggests that a new method remains to be described that will make full use of the topology and node times for improved transmission time inference.

Inferring viral transmission time from phylogenies for known transmission pairs

When the time of an HIV transmission event is unknown, methods to identify it from virus genetic data can reveal the circumstances that enable transmission. We developed a single-parameter Markov model to infer transmission time from an HIV phylogeny constructed of multiple virus sequences from people in a transmission pair. Our method finds the statistical support for transmission occurring in different possible time slices. We compared our time-slice model results to previously-described methods: a tree-based logical transmission interval, a simple parsimony-like rules-based method, and a more complex coalescent model. Across simulations with multiple transmitted lineages, different transmission times relative to the source's infection, and different sampling times relative to transmission, we found that overall our time-slice model provided accurate and narrower estimates of the time of transmission. We also identified situations when transmission time or direction was difficult to estimate by any method, particularly when transmission occurred long after the source was infected and when sampling occurred long after transmission. Applying our model to real HIV transmission pairs showed some agreement with facts known from the case investigations. We also found, however, that uncertainty on the inferred transmission time was driven more by uncertainty from time-calibration of the phylogeny than from the model inference itself. Encouragingly, comparable performance of the Markov time-slice model and the coalescent model-which make use of different information within a tree-suggests that a new method remains to be described that will make full use of the topology and node times for improved transmission time inference.

HIV-1 drug resistance and genetic transmission network among newly diagnosed people living with HIV/AIDS in Ningbo, China between 2018 and 2021

BACKGROUND

As the HIV epidemic continues to grow, transmitted drug resistance(TDR) and determining relationship of HIV transmission are major barriers to reduce the risk of HIV transmissions.This study aimed to examine the molecular epidemiology and TDR and evaluated the transmission pattern among newly diagnosed people living with HIV/AIDS(PLWHA) in Ningbo city, which could contribute to the development of targeted precision interventions.

METHODS

Consecutive cross-sectional surveys were conducted in Ningbo City between January 2018 and December 2021. The HIV-1 pol gene region was amplified and sequenced for drug resistance and genetic transmission network analysis. TDR was determined using the Stanford University HIV Drug Resistance Database. Genetic transmission network was visualized using Cytoscape with the genetic distance threshold of 0.013.

RESULTS

A total of 1006 sequences were sequenced successfully, of which 61 (6.1%) showed evidence of TDR. The most common mutations were K103N (2.3%), E138A/G/Q (1.7%) and V179D/E (1.2%). 12 HIV-1 genotypes were identified, with CRF07_BC being the major genotype (43.3%, 332/767), followed by CRF01_AE (33.7%, 339/1006). 444 (44.1%) pol sequences formed 856 links within 120 transmission clusters in the network. An increasing trend in clustering rate between 2018 and 2021(χ2 = 9.546, P = 0.023) was observed. The odds of older age (≥ 60 years:OR = 2.038, 95%CI = 1.072 ~ 3.872, compared to < 25 years), HIV-1 genotypes (CRF07_BC: OR = 2.147, 95%CI = 1.582 ~ 2.914; CRF55_01B:OR = 2.217, 95%CI = 1.201 ~ 4.091, compared to CRF01_AE) were significantly related to clustering. Compared with CRF01_AE, CRF07_BC were prone to form larger clusters. The largest cluster with CRF07_BC was increased from 15 cases in 2018 to 83 cases in 2021.

CONCLUSIONS

This study revealed distribution of HIV-1 genotypes, and genetic transmission network were diverse and complex in Ningbo city. The prevalence of TDR was moderate, and NVP and EFV were high-level NNRTI resistance. Individuals aged ≥ 60 years old were more easily detected in the networks and CRF07_BC were prone to form rapid growth and larger clusters. These date suggested that surveillance and comprehensive intervention should be designed for key rapid growth clusters to reduce the potential risk factors of HIV-1 transmission.

Identifying potential significant factors impacting zero-inflated proportion data

Classical supervised methods like linear regression and decision trees are not completely adapted for identifying impacting factors on a response variable corresponding to zero-inflated proportion data (ZIPD) that are dependent, continuous and bounded. In this article we propose a within-block permutation-based methodology to identify factors (discrete or continuous) that are significantly correlated with ZIPD, we propose a performance indicator quantifying the percentage of correlation explained by the subset of significant factors, and we show how to predict the ranks of the response variables conditionally on the observation of these factors. The methodology is illustrated on simulated data and on two real data sets dealing with epidemiology. In the first data set, ZIPD correspond to probabilities of transmission of Influenza between horses. In the second data set, ZIPD correspond to probabilities that geographic entities (eg, states and countries) have the same COVID-19 mortality dynamics.

Recombination smooths the time signal disrupted by latency in within-host HIV phylogenies

Within-host Human immunodeficiency virus (HIV) evolution involves several features that may disrupt standard phylogenetic reconstruction. One important feature is reactivation of latently integrated provirus, which has the potential to disrupt the temporal signal, leading to variation in the branch lengths and apparent evolutionary rates in a tree. Yet, real within-host HIV phylogenies tend to show clear, ladder-like trees structured by the time of sampling. Another important feature is recombination, which violates the fundamental assumption that evolutionary history can be represented by a single bifurcating tree. Thus, recombination complicates the within-host HIV dynamic by mixing genomes and creating evolutionary loop structures that cannot be represented in a bifurcating tree. In this paper, we develop a coalescent-based simulator of within-host HIV evolution that includes latency, recombination, and effective population size dynamics that allows us to study the relationship between the true, complex genealogy of within-host HIV evolution, encoded as an ancestral recombination graph (ARG), and the observed phylogenetic tree. To compare our ARG results to the familiar phylogeny format, we calculate the expected bifurcating tree after decomposing the ARG into all unique site trees, their combined distance matrix, and the overall corresponding bifurcating tree. While latency and recombination separately disrupt the phylogenetic signal, remarkably, we find that recombination recovers the temporal signal of within-host HIV evolution caused by latency by mixing fragments of old, latent genomes into the contemporary population. In effect, recombination averages over extant heterogeneity, whether it stems from mixed time signals or population bottlenecks. Furthermore, we establish that the signals of latency and recombination can be observed in phylogenetic trees despite being an incorrect representation of the true evolutionary history. Using an approximate Bayesian computation method, we develop a set of statistical probes to tune our simulation model to nine longitudinally sampled within-host HIV phylogenies. Because ARGs are exceedingly difficult to infer from real HIV data, our simulation system allows investigating effects of latency, recombination, and population size bottlenecks by matching decomposed ARGs to real data as observed in standard phylogenies.

No Difference in the Prevalence of HIV-1 gag Cytotoxic T-Lymphocyte-Associated Escape Mutations in Viral Sequences from Early and Late Parts of the HIV-1 Subtype C Pandemic in Botswana

HIV is known to accumulate escape mutations in the gag gene in response to the immune response from cytotoxic T lymphocytes (CTLs). These mutations can occur within an individual as well as at a population level. The population of Botswana exhibits a high prevalence of HLA*B57 and HLA*B58, which are associated with effective immune control of HIV. In this retrospective cross-sectional investigation, HIV-1 gag gene sequences were analyzed from recently infected participants across two time periods which were 10 years apart: the early time point (ETP) and late time point (LTP). The prevalence of CTL escape mutations was relatively similar between the two time points-ETP (10.6%) and LTP (9.7%). The P17 protein had the most mutations (9.4%) out of the 36 mutations that were identified. Three mutations (A83T, K18R, Y79H) in P17 and T190A in P24 were unique to the ETP sequences at a prevalence of 2.4%, 4.9%, 7.3%, and 5%, respectively. Mutations unique to the LTP sequences were all in the P24 protein, including T190V (3%), E177D (6%), R264K (3%), G248D (1%), and M228L (11%). Mutation K331R was statistically higher in the ETP (10%) compared to the LTP (1%) sequences (p < 0.01), while H219Q was higher in the LTP (21%) compared to the ETP (5%) (p < 0.01). Phylogenetically, the gag sequences clustered dependently on the time points. We observed a slower adaptation of HIV-1C to CTL immune pressure at a population level in Botswana. These insights into the genetic diversity and sequence clustering of HIV-1C can aid in the design of future vaccine strategies.

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.

A deep learning approach to real-time HIV outbreak detection using genetic data

Pathogen genomic sequence data are increasingly made available for epidemiological monitoring. A main interest is to identify and assess the potential of infectious disease outbreaks. While popular methods to analyze sequence data often involve phylogenetic tree inference, they are vulnerable to errors from recombination and impose a high computational cost, making it difficult to obtain real-time results when the number of sequences is in or above the thousands. Here, we propose an alternative strategy to outbreak detection using genomic data based on deep learning methods developed for image classification. The key idea is to use a pairwise genetic distance matrix calculated from viral sequences as an image, and develop convolutional neutral network (CNN) models to classify areas of the images that show signatures of active outbreak, leading to identification of subsets of sequences taken from an active outbreak. We showed that our method is efficient in finding HIV-1 outbreaks with R0 ≥ 2.5, and overall a specificity exceeding 98% and sensitivity better than 92%. We validated our approach using data from HIV-1 CRF01 in Europe, containing both endemic sequences and a well-known dual outbreak in intravenous drug users. Our model accurately identified known outbreak sequences in the background of slower spreading HIV. Importantly, we detected both outbreaks early on, before they were over, implying that had this method been applied in real-time as data became available, one would have been able to intervene and possibly prevent the extent of these outbreaks. This approach is scalable to processing hundreds of thousands of sequences, making it useful for current and future real-time epidemiological investigations, including public health monitoring using large databases and especially for rapid outbreak identification.

Characterizing viral within-host diversity in fast and non-equilibrium demo-genetic dynamics

High-throughput sequencing has opened the route for a deep assessment of within-host genetic diversity that can be used, e.g., to characterize microbial communities and to infer transmission links in infectious disease outbreaks. The performance of such characterizations and inferences cannot be analytically assessed in general and are often grounded on computer-intensive evaluations. Then, being able to simulate within-host genetic diversity across time under various demo-genetic assumptions is paramount to assess the performance of the approaches of interest. In this context, we built an original model that can be simulated to investigate the temporal evolution of genotypes and their frequencies under various demo-genetic assumptions. The model describes the growth and the mutation of genotypes at the nucleotide resolution conditional on an overall within-host viral kinetics, and can be tuned to generate fast non-equilibrium demo-genetic dynamics. We ran simulations of this model and computed classic diversity indices to characterize the temporal variation of within-host genetic diversity (from high-throughput amplicon sequences) of virus populations under three demographic kinetic models of viral infection. Our results highlight how demographic (viral load) and genetic (mutation, selection, or drift) factors drive variations in within-host diversity during the course of an infection. In particular, we observed a non-monotonic relationship between pathogen population size and genetic diversity, and a reduction of the impact of mutation on diversity when a non-specific host immune response is activated. The large variation in the diversity patterns generated in our simulations suggests that the underlying model provides a flexible basis to produce very diverse demo-genetic scenarios and test, for instance, methods for the inference of transmission links during outbreaks.

Combining biomarker and virus phylogenetic models improves HIV-1 epidemiological source identification

To identify and stop active HIV transmission chains new epidemiological techniques are needed. Here, we describe the development of a multi-biomarker augmentation to phylogenetic inference of the underlying transmission history in a local population. HIV biomarkers are measurable biological quantities that have some relationship to the amount of time someone has been infected with HIV. To train our model, we used five biomarkers based on real data from serological assays, HIV sequence data, and target cell counts in longitudinally followed, untreated patients with known infection times. The biomarkers were modeled with a mixed effects framework to allow for patient specific variation and general trends, and fit to patient data using Markov Chain Monte Carlo (MCMC) methods. Subsequently, the density of the unobserved infection time conditional on observed biomarkers were obtained by integrating out the random effects from the model fit. This probabilistic information about infection times was incorporated into the likelihood function for the transmission history and phylogenetic tree reconstruction, informed by the HIV sequence data. To critically test our methodology, we developed a coalescent-based simulation framework that generates phylogenies and biomarkers given a specific or general transmission history. Testing on many epidemiological scenarios showed that biomarker augmented phylogenetics can reach 90% accuracy under idealized situations. Under realistic within-host HIV-1 evolution, involving substantial within-host diversification and frequent transmission of multiple lineages, the average accuracy was at about 50% in transmission clusters involving 5-50 hosts. Realistic biomarker data added on average 16 percentage points over using the phylogeny alone. Using more biomarkers improved the performance. Shorter temporal spacing between transmission events and increased transmission heterogeneity reduced reconstruction accuracy, but larger clusters were not harder to get right. More sequence data per infected host also improved accuracy. We show that the method is robust to incomplete sampling and that adding biomarkers improves reconstructions of real HIV-1 transmission histories. The technology presented here could allow for better prevention programs by providing data for locally informed and tailored strategies.

Transmitted HIV-1 is more virulent in heterosexual individuals than men-who-have-sex-with-men

Transmission bottlenecks introduce selection pressures on HIV-1 that vary with the mode of transmission. Recent studies on small cohorts have suggested that stronger selection pressures lead to fitter transmitted/founder (T/F) strains. Manifestations of this selection bias at the population level have remained elusive. Here, we analysed early CD4 cell count measurements reported from ∼340,000 infected heterosexual individuals (HET) and men-who-have-sex-with-men (MSM), across geographies, ethnicities and calendar years. The reduction in CD4 counts early in infection is reflective of the virulence of T/F strains. MSM and HET use predominant modes of transmission, namely, anal and penile-vaginal, with among the largest differences in the selection pressures at transmission across modes. Further, in most geographies, the groups show little inter-mixing, allowing for the differential selection bias to be sustained and amplified. We found that the early reduction in CD4 counts was consistently greater in HET than MSM (P<0.05). To account for inherent variations in baseline CD4 counts, we constructed a metric to quantify the extent of progression to AIDS as the ratio of the reduction in measured CD4 counts from baseline and the reduction associated with AIDS. We found that this progression corresponding to the early CD4 measurements was ∼68% for MSM and ∼87% for HET on average (P<10⁻⁴; Cohen’s d, d_s = 0.36), reflecting the more severe disease caused by T/F strains in HET than MSM at the population level. Interestingly, the set-point viral load was not different between the groups (d_s<0.12), suggesting that MSM were more tolerant and not more resistant to their T/F strains than HET. This difference remained when we controlled for confounding factors using multivariable regression. We concluded that the different selection pressures at transmission have resulted in more virulent T/F strains in HET than MSM. These findings have implications for our understanding of HIV-1 pathogenesis, evolution, and epidemiology.

HIV-1 encounters a key bottleneck at the time of its transmission from one individual to another. This transmission bottleneck can differ between modes of transmission. The stronger this bottleneck is, the more fit the virus has to be to be successfully transmitted. Accordingly, the transmitted/founder (T/F) strains of HIV-1 may have different fitness in risk groups that use different modes of transmission. While studies on small cohorts do support this notion, observations of the manifestations of this differential selection bias at the population level have been lacking. Here, we examined reported early CD4 count measurements from ∼340,000 HET and MSM, across geographies, ethnicities, and calendar years. Early CD4 counts are a measure of the severity of the infection due to T/F strains. HET and MSM transmit predominantly via penile-vaginal and anal modes, respectively, and do not inter-mix significantly. Remarkably, we found that HET consistently had lower early CD4 counts than MSM. This difference could not be attributed to potential confounding factors, such as set-point viral load. The difference thus provided evidence that T/F strains had evolved to be more virulent in HET than MSM at the population level. Intervention strategies may benefit from accounting for this difference between risk groups.

Methods Combining Genomic and Epidemiological Data in the Reconstruction of Transmission Trees: A Systematic Review

In order to better understand transmission dynamics and appropriately target control and preventive measures, studies have aimed to identify who-infected-whom in actual outbreaks. Numerous reconstruction methods exist, each with their own assumptions, types of data, and inference strategy. Thus, selecting a method can be difficult. Following PRISMA guidelines, we systematically reviewed the literature for methods combing epidemiological and genomic data in transmission tree reconstruction. We identified 22 methods from the 41 selected articles. We defined three families according to how genomic data was handled: a non-phylogenetic family, a sequential phylogenetic family, and a simultaneous phylogenetic family. We discussed methods according to the data needed as well as the underlying sequence mutation, within-host evolution, transmission, and case observation. In the non-phylogenetic family consisting of eight methods, pairwise genetic distances were estimated. In the phylogenetic families, transmission trees were inferred from phylogenetic trees either simultaneously (nine methods) or sequentially (five methods). While a majority of methods (17/22) modeled the transmission process, few (8/22) took into account imperfect case detection. Within-host evolution was generally (7/8) modeled as a coalescent process. These practical and theoretical considerations were highlighted in order to help select the appropriate method for an outbreak.

The performance of outgroup-free rooting under evolutionary radiations

Tree rooting implies a temporal dimension to phylogenies. Only after defining the position of the root node is that the ancestral-descendant relationship between branches can be fully deduced. Rooting has been usually carried out by employing evolutionarily close outgroup lineages, which is a drawback when these lineages are unavailable or unknown. Alternatively, outgroup-free rooting methods were proposed, which rely on the constancy of evolutionary rates to varying degrees. In this work we analyzed the performance of two of these methods, the midpoint rooting (MPR) and the minimal ancestor deviation (MAD), in rooting topologies evolved under challenging scenarios of fast evolutionary radiations derived from empirical data, characterized by short internal branches near the crown node. Considering all branch length combinations investigated, both methods exhibited average success rates below 50%, although MAD slightly outperformed MPR. Moreover, tree balance significantly impacted the relative performance of the methods. We found that, in four-taxa unrooted trees, the outcome of whether both methodologies will correctly root the tree can be roughly predicted by two simple dimensionless metrics: the coefficient of variation of the external branch lengths, and the ratio between the internal branch length to the total sum of branch lengths, which were employed to devise a general linear model that allowed calculating the probability of correct placing the root node for any four-taxa tree. We predicted that the performance of both outgroup-free rooting methods on loci representing the placental mammal radiation ranged between 50% and 75%.

Identification of Genetically Related HCV Infections Among Self-Described Injecting Partnerships

BACKGROUND

The current opioid epidemic across the United States has fueled a surge in the rate of new hepatitis C virus (HCV) infections among young persons who inject drugs (PWIDs). Paramount to interrupting transmission is targeting these high-risk populations and understanding the underlying network structures facilitating transmission within these communities.

METHODS

Deep sequencing data were obtained for 52 participants from 32 injecting partnerships enrolled in the U-Find-Out (UFO) Partner Study, which is a prospective study of self-described injecting dyad partnerships from a large community-based study of HCV infection in young adult PWIDs from San Francisco. Phylogenetically linked transmission events were identified using traditional genetic-distance measures and viral deep sequence phylogenies reconstructed to determine the statistical support of inferences and the direction of transmission within partnerships.

RESULTS

Using deep sequencing data, we found that 12 of 32 partnerships were genetically similar and clustered. Three additional phylogenetic clusters were found describing novel putative transmission links outside of the injecting relationship. Transmission direction was inferred correctly for 5 partnerships with the incorrect transmission direction inferred in more than 50% of cases. Notably, we observed that phylogenetic linkage was most often associated with a lower number of network partners and involvement in a sexual relationship.

CONCLUSIONS

Deep sequencing of HCV among self-described injecting partnerships demonstrates that the majority of transmission events originate from outside of the injecting partnership. Furthermore, these findings caution that phylogenetic methods may be unable to routinely infer the direction of transmission among PWIDs especially when transmission events occur in rapid succession within high-risk networks.

The inference of HIV-1 transmission direction between a man who has sex with men and his heterosexual wife based on the sequences of HIV-1 quasi-species

Currently, homosexual transmission has become one of the main routes of HIV-1 spread in China. Furthermore, about 80% Chinese men, who have sex with men (MSM), feel forced to enter eventually into heterosexual marriages due to the Chinese traditional marriage culture, which may cause HIV-1 infection in families. In this study, we identified HIV-1 transmission in a family and the direction of HIV-1 transmission from a MSM to his wife and infant, which indicated Chinese MSM may have become a potential bridge of HIV-1 transmission to their wives and children. Therefore, we need to develop more effective defence measures to prevent the spread of HIV-1 in MSM families in China.

HIV Care Prioritization Using Phylogenetic Branch Length

BACKGROUND

The structure of the HIV transmission networks can be dictated by just a few individuals. Public health intervention, such as ensuring people living with HIV adhere to antiretroviral therapy and remain virally suppressed, can help control the spread of the virus. However, such intervention requires using limited public health resource allocations. Determining which individuals are most at risk of transmitting HIV could allow public health officials to focus their limited resources on these individuals.

SETTING

Molecular epidemiology can help prioritize people living with HIV by patterns of transmission inferred from their sampled viral sequences. Such prioritization has been previously suggested and performed by monitoring cluster growth. In this article, we introduce Prioritization using AnCesTral edge lengths (ProACT), a phylogenetic approach for prioritizing individuals living with HIV.

METHODS

ProACT starts from a phylogeny inferred from sequence data and orders individuals according to their terminal branch length, breaking ties using ancestral branch lengths. We evaluated ProACT on a real data set of 926 HIV-1 subtype B pol data obtained in San Diego between 2005 and 2014 and a simulation data set modeling the same epidemic. Prioritization methods are compared by their ability to predict individuals who transmit most after the prioritization.

RESULTS

Across all simulation conditions and most real data sampling conditions, ProACT outperformed monitoring cluster growth for multiple metrics of prioritization efficacy.

CONCLUSION

The simple strategy used by ProACT improves the effectiveness of prioritization compared with state-of-the-art methods that rely on monitoring the growth of transmission clusters defined based on genetic distance.

Evaluation of Phylogenetic Methods for Inferring the Direction of Human Immunodeficiency Virus (HIV) Transmission: HIV Prevention Trials Network (HPTN) 052

BACKGROUND

Phylogenetic analysis can be used to assess human immunodeficiency virus (HIV) transmission in populations. We inferred the direction of HIV transmission using whole-genome HIV sequences from couples with known linked infection and known transmission direction.

METHODS

Complete next-generation sequencing (NGS) data were obtained for 105 unique index-partner sample pairs from 32 couples enrolled in the HIV Prevention Trials Network (HPTN) 052 study (up to 2 samples/person). Index samples were obtained up to 5.5 years before partner infection; partner samples were obtained near the time of seroconversion. The bioinformatics method, phyloscanner, was used to infer transmission direction. Analyses were performed using samples from individual sample pairs, samples from all couples (1 sample/person; group analysis), and all available samples (multisample group analysis). Analysis was also performed using NGS data from defined regions of the HIV genome (gag, pol, env).

RESULTS

Using whole-genome NGS data, transmission direction was inferred correctly (index to partner) for 98 of 105 (93.3%) of the individual sample pairs, 99 of 105 (94.3%) sample pairs using group analysis, and 31 of the 32 couples (96.9%) using multisample group analysis. There were no cases where the incorrect transmission direction (partner to index) was inferred. The accuracy of the method was higher with greater time between index and partner sample collection. Pol region sequences performed better than env or gag sequences for inferring transmission direction.

CONCLUSIONS

We demonstrate the potential of a phylogenetic method to infer the direction of HIV transmission between 2 individuals using whole-genome and pol NGS data.

Using longitudinally sampled viral nucleotide sequences to characterize the drivers of HIV-1 transmission

OBJECTIVES

Understanding the drivers of HIV-1 transmission is of importance for curbing the ongoing epidemic. Phylogenetic methods based on single viral sequences allow us to assess whether two individuals are part of the same viral outbreak, but cannot on their own assess who potentially transmitted the virus. We developed and assessed a molecular epidemiology method with the main aim to screen cohort studies for and to characterize individuals who are 'potential HIV-1 transmitters', in order to understand the drivers of HIV-1 transmission.

METHODS

We developed and validated a molecular epidemiology approach using longitudinally sampled viral Sanger sequences to characterize potential HIV-1 transmitters in the Swiss HIV Cohort Study.

RESULTS

Our method was able to identify 279 potential HIV-1 transmitters and allowed us to determine the main epidemiological and virological drivers of transmission. We found that the directionality of transmission was consistent with infection times for 72.9% of 85 potential HIV-1 transmissions with accurate infection date estimates. Being a potential HIV-1 transmitter was associated with risk factors including viral load [adjusted odds ratio_{multivariable} (95% confidence interval): 1.86 (1.49-2.32)], syphilis coinfection [1.52 (1.06-2.19)], and recreational drug use [1.45 (1.06-1.98)]. By contrast for the potential HIV-1 recipients, this association was weaker or even absent [1.18 (0.82-1.72), 0.89 (0.52-1.55) and 1.53 (0.98-2.39), respectively], indicating that inferred directionality of transmission is useful at the population level.

CONCLUSIONS

Our results indicate that longitudinally sampled Sanger sequences do not provide sufficient information to identify transmitters with high certainty at the individual level, but that they allow the drivers of transmission at the population level to be characterized.

What Should Health Departments Do with HIV Sequence Data?

Many countries and US states have mandatory statues that require reporting of HIV clinical data including genetic sequencing results to the public health departments. Because genetic sequencing is a part of routine care for HIV infected persons, health departments have extensive sequence collections spanning years and even decades of the HIV epidemic. How should these data be used (or not) in public health practice? This is a complex, multi-faceted question that weighs personal risks against public health benefit. The answer is neither straightforward nor universal. However, to make that judgement—of how genetic sequence data should be used in describing and combating the HIV epidemic—we need a clear image of what a phylogenetically enhanced HIV surveillance system can do and what benefit it might provide. In this paper, we present a positive case for how up-to-date analysis of HIV sequence databases managed by health departments can provide unique and actionable information of how HIV is spreading in local communities. We discuss this question broadly, with examples from the US, as it is globally relevant for all health authorities that collect HIV genetic data.

Broadly neutralizing plasma antibodies effective against autologous circulating viruses in infants with multivariant HIV-1 infection

Broadly neutralizing antibodies (bnAbs) develop in a subset of HIV-1 infected individuals over 2-3 years of infection. Infected infants develop plasma bnAbs frequently and as early as 1-year post-infection suggesting factors governing bnAb induction in infants are distinct from adults. Understanding viral characteristics in infected infants with early bnAb responses will provide key information about antigenic triggers driving B cell maturation pathways towards induction of bnAbs. Herein, we evaluate the presence of plasma bnAbs in a cohort of 51 HIV-1 clade-C infected infants and identify viral factors associated with early bnAb responses. Plasma bnAbs targeting V2-apex on the env are predominant in infant elite and broad neutralizers. Circulating viral variants in infant elite neutralizers are susceptible to V2-apex bnAbs. In infant elite neutralizers, multivariant infection is associated with plasma bnAbs targeting diverse autologous viruses. Our data provides information supportive of polyvalent vaccination approaches capable of inducing V2-apex bnAbs against HIV-1.

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.

FAVITES: simultaneous simulation of transmission networks, phylogenetic trees and sequences

MOTIVATION

The ability to simulate epidemics as a function of model parameters allows insights that are unobtainable from real datasets. Further, reconstructing transmission networks for fast-evolving viruses like Human Immunodeficiency Virus (HIV) may have the potential to greatly enhance epidemic intervention, but transmission network reconstruction methods have been inadequately studied, largely because it is difficult to obtain 'truth' sets on which to test them and properly measure their performance.

RESULTS

We introduce FrAmework for VIral Transmission and Evolution Simulation (FAVITES), a robust framework for simulating realistic datasets for epidemics that are caused by fast-evolving pathogens like HIV. FAVITES creates a generative model to produce contact networks, transmission networks, phylogenetic trees and sequence datasets, and to add error to the data. FAVITES is designed to be extensible by dividing the generative model into modules, each of which is expressed as a fixed API that can be implemented using various models. We use FAVITES to simulate HIV datasets and study the realism of the simulated datasets. We then use the simulated data to study the impact of the increased treatment efforts on epidemiological outcomes. We also study two transmission network reconstruction methods and their effectiveness in detecting fast-growing clusters.

AVAILABILITY AND IMPLEMENTATION

FAVITES is available at https://github.com/niemasd/FAVITES, and a Docker image can be found on DockerHub (https://hub.docker.com/r/niemasd/favites).

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Inferring epidemiological links from deep sequencing data: a statistical learning approach for human, animal and plant diseases

Pathogen sequence data have been exploited to infer who infected whom, by using empirical and model-based approaches. Most of these approaches exploit one pathogen sequence per infected host (e.g. individual, household, field). However, modern sequencing techniques can reveal the polymorphic nature of within-host populations of pathogens. Thus, these techniques provide a subsample of the pathogen variants that were present in the host at the sampling time. Such data are expected to give more insight on epidemiological links than a single sequence per host. In general, a mechanistic viewpoint to transmission and micro-evolution has been followed to infer epidemiological links from these data. Here, we investigate an alternative approach grounded on statistical learning. The idea consists of learning the structure of epidemiological links with a pseudo-evolutionary model applied to training data obtained from contact tracing, for example, and using this initial stage to infer links for the whole dataset. Such an approach has the potential to be particularly valuable in the case of a risk of erroneous mechanistic assumptions, it is sufficiently parsimonious to allow the handling of big datasets in the future, and it is versatile enough to be applied to very different contexts from animal, human and plant epidemiology.

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’.

Phylogenetics in HIV transmission: taking within-host diversity into account

PURPOSE OF REVIEW

Within-host diversity complicates transmission models because it recognizes that between-host virus phylogenies are not identical to the transmission history among the infected hosts. This review presents the biological and theoretical foundations for recent development in this field, and shows that modern phylodynamic methods are capable of inferring realistic transmission histories from HIV sequence data.

RECENT FINDINGS

Transmission of single or multiple genetic variants from a donor's HIV population results in donor-recipient phylogenies with combinations of monophyletic, paraphyletic, and polyphyletic patterns. Large-scale simulations and analyses of many real HIV datasets have established that transmission direction, directness, or common source often can be inferred based on HIV sequence data. Phylodynamic reconstruction of HIV transmissions that include within-host HIV diversity have recently been established and made available in several software packages.

SUMMARY

Phylodynamic methods that include realistic features of HIV genetic diversification have come of age, significantly improving inference of key epidemiological parameters. This opens the door to more accurate surveillance and better-informed prevention campaigns.

Evaluation of A Phylogenetic Pipeline to Examine Transmission Networks in A Canadian HIV Cohort

Keywords: HIV; Canada; molecular phylogenetics; viral evolution; person-to-person transmission inference; transmission network; summary statistics.

Source identification of HIV-1 transmission in three lawsuits Using Ultra-Deep pyrosequencing and phylogenetic analysis

BACKGROUND/PURPOSE

Intentional transmission of HIV-1 is a crime. Identifying the source of transmission between HIV-1 infected cases using phylogenetic analysis has limitations, including delayed examinations after the initiation of infection and ambiguity of phyletic relationships. This study was the first to introduce phylogenetic tree Results as forensic evidence in a trial in Taiwan.

METHODS

Three lawsuit cases from different district courts in Taiwan were chosen for this study. We identified the source of transmission between individuals in each lawsuit based on the maximum likelihood and Bayesian phylogenetic tree analyses using the HIV-1 sequences from molecular cloning and ultra-deep pyrosequencing (UDPS). Two gene regions of the HIV genome, env and gag, were involved.

RESULTS

The results of phylogenetic analysis using sequences from molecular cloning were clear and evidential enough in lawsuits 1 and 3. Due to the delayed sampling time, the result of sequences from molecular cloning in lawsuit 2 was ambiguous. Combined with the analyzed result of sequences from UDPS and epidemiological information, the source of transmission in lawsuit 2 was further identified.

CONCLUSION

Hence phylogenetic analyses cannot exclude the possibility of unsampled intermediaries, the data interpretation should be more careful and conservative, and it should not be considered as the only evidence for the source identification in a trial without epidemiological or serological information. The evaluation of the introduced UDPS method in the identification of transmission source has shown that the validity and evidential effects were still limited and need further optimization.

High-throughput sequencing (HTS) for the analysis of viral populations

The development of High-Throughput Sequencing (HTS) technologies is having a major impact on the genomic analysis of viral populations. Current HTS platforms can capture nucleic acid variation across millions of genes for both selected amplicons and full viral genomes. HTS has already facilitated the discovery of new viruses, hinted new taxonomic classifications and provided a deeper and broader understanding of their diversity, population and genetic structure. Hence, HTS has already replaced standard Sanger sequencing in basic and applied research fields, but the next step is its implementation as a routine technology for the analysis of viruses in clinical settings. The most likely application of this implementation will be the analysis of viral genomics, because the huge population sizes, high mutation rates and very fast replacement of viral populations have demonstrated the limited information obtained with Sanger technology. In this review, we describe new technologies and provide guidelines for the high-throughput sequencing and genetic and evolutionary analyses of viral populations and metaviromes, including software applications. With the development of new HTS technologies, new and refurbished molecular and bioinformatic tools are also constantly being developed to process and integrate HTS data. These allow assembling viral genomes and inferring viral population diversity and dynamics. Finally, we also present several applications of these approaches to the analysis of viral clinical samples including transmission clusters and outbreak characterization.

Phylogenetic Methods Inconsistently Predict the Direction of HIV Transmission Among Heterosexual Pairs in the HPTN 052 Cohort

BACKGROUND

We evaluated use of phylogenetic methods to predict the direction of human immunodeficiency virus (HIV) transmission.

METHODS

For 33 pairs of HIV-infected patients (hereafter, "index patients") and their partners who acquired genetically linked HIV infection during the study, samples were collected from partners and index patients close to the time when the partner seroconverted (hereafter, "SC samples"); for 31 pairs, samples collected from the index patient at an earlier time point (hereafter, "early index samples") were also available. Phylogenies were inferred using env next-generation sequences (1 tree per pair/subtype). The direction of transmission (DoT) predicted from each tree was classified as correct or incorrect on the basis of which sequences (those from the index patient or the partner) were closest to the root. DoT was also assessed using maximum parsimony to infer ancestral node states for 100 bootstrap trees.

RESULTS

DoT was predicted correctly for both single-pair and subtype-specific trees in 22 pairs (67%) by using SC samples and in 23 pairs (74%) by using early index samples. DoT was predicted incorrectly for 4 pairs (15%) by using SC or early index samples. In the bootstrap analysis, DoT was predicted correctly for 18 pairs (55%) by using SC samples and for 24 pairs (73%) by using early index samples. DoT was predicted incorrectly for 7 pairs (21%) by using SC samples and for 4 pairs (13%) by using early index samples.

CONCLUSIONS

Phylogenetic methods based solely on the tree topology of HIV env sequences, particularly without consideration of phylogenetic uncertainty, may be insufficient for determining DoT.

Ethical issues in HIV phylogenetics and molecular epidemiology

PURPOSE OF REVIEW

HIV phylogenetic and molecular epidemiology analyses are increasingly being performed with a goal of improving HIV prevention efforts. However, ethical, legal and social issues are associated with these analyses, and should be considered when performed.

RECENT FINDINGS

Several working groups have recently outlined the major issues surrounding the use of molecular epidemiology for HIV prevention. First, the benefits of HIV molecular epidemiology remain unclear, and further work is needed to quantitatively demonstrate the benefits that can be expected. Second, privacy loss is an important risk, with implications of disclosure varying by the regional legal and social climate. Inferential privacy risks will increase with technological improvements in sequencing and analysis. Third, data sharing, which enhances the utility of the data, may also increase the risk of inferential privacy loss. Mitigation strategies are available to address each of these issues.

SUMMARY

HIV molecular epidemiology for research and public health pose significant ethical issues that continue to evolve with improving technology, increased sampling and a changing legal and social climate. Guidance surrounding these issues needs to be developed for researchers and public health officials in an iterative and region specific manner that accounts for the potential benefits and risks of this technology.

Inferring HIV-1 transmission networks and sources of epidemic spread in Africa with deep-sequence phylogenetic analysis

To prevent new infections with human immunodeficiency virus type 1 (HIV-1) in sub-Saharan Africa, UNAIDS recommends targeting interventions to populations that are at high risk of acquiring and passing on the virus. Yet it is often unclear who and where these 'source' populations are. Here we demonstrate how viral deep-sequencing can be used to reconstruct HIV-1 transmission networks and to infer the direction of transmission in these networks. We are able to deep-sequence virus from a large population-based sample of infected individuals in Rakai District, Uganda, reconstruct partial transmission networks, and infer the direction of transmission within them at an estimated error rate of 16.3% [8.8-28.3%]. With this error rate, deep-sequence phylogenetics cannot be used against individuals in legal contexts, but is sufficiently low for population-level inferences into the sources of epidemic spread. The technique presents new opportunities for characterizing source populations and for targeting of HIV-1 prevention interventions in Africa.