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.

The Number and Pattern of Viral Genomic Reassortments are not Necessarily Identifiable from Segment Trees.

Reassortment is an evolutionary process common in viruses with segmented genomes. These viruses can swap whole genomic segments during cellular co-infection, giving rise to novel progeny formed from the mixture of parental segments. Since large-scale genome rearrangements have the potential to generate new phenotypes, reassortment is important to both evolutionary biology and public health research. However, statistical inference of the pattern of reassortment events from phylogenetic data is exceptionally difficult, potentially involving inference of general graphs in which individual segment trees are embedded. In this paper, we argue that, in general, the number and pattern of reassortment events are not identifiable from segment trees alone, even with theoretically ideal data. We call this fact the fundamental problem of reassortment, which we illustrate using the concept of the "first-infection tree," a potentially counterfactual genealogy that would have been observed in the segment trees had no reassortment occurred. Further, we illustrate four additional problems that can arise logically in the inference of reassortment events and show, using simulated data, that these problems are not rare and can potentially distort our observation of reassortment even in small data sets. Finally, we discuss how existing methods can be augmented or adapted to account for not only the fundamental problem of reassortment, but also the four additional situations that can complicate the inference of reassortment.

Identification of two novel subtypes of hepatitis C virus genotype 8 and a potential new genotype successfully treated with direct acting antivirals.

BACKGROUND: Hepatitis C virus (HCV) has a high genetic diversity and is classified into 8 genotypes and over 90 subtypes with some endemic to specific world regions. This could compromise direct-acting antiviral (DAA) efficacy and global HCV elimination. METHODS: We characterised HCV subtypes 'rare' to the UK (non-1a/1b/2b/3a/4d) by whole genome sequencing via a national surveillance programme. Genetic analyses to determine the genotype of samples with unresolved genotypes were undertaken by comparison with ICTV HCV reference sequences. RESULTS: Two HCV variants were characterised as being closely related to the recently identified genotype 8 (GT8), with >85% pairwise genetic distance similarity to GT8 sequences and within the typical inter-subtype genetic distance range. The individuals infected by the variants were UK residents originally from Pakistan and India. In contrast, a third variant was only confidently identified to be more similar to GT6 compared to other genotypes across 6% of the genome and was isolated from a UK resident originally from Guyana. All three were cured with pangenotypic DAAs (Sofosbuvir + Velpatasvir or Glecaprevir + Pibrentasvir) despite the presence of resistance polymorphisms in NS3 (80 K/168E), NS5A (28 V/30S/62L/92S/93S) and NS5B (159F). CONCLUSIONS: This study expands our knowledge of HCV diversity by identifying two new GT8 subtypes and potentially a new genotype.

Eddy Current Position Measurement in Harsh Environments: A Temperature Compensation and Calibration Approach.

Eddy current displacement sensors (ECDSs) are widely used for the noncontact position measurement of small displacements (lift-offs). Challenges arise with larger displacements as the sensitivity of the ECDSs decreases. This leads to a more pronounced impact of temperature variations on the inductance and, consequently, an increased position error. Design solutions often rely on multiple coils, suitable coil carrier materials, and compensation measures to address the challenges. This study presents a single-coil ECDS for large displacement ranges in environments with high temperatures and temperature variations. The analysis is based on a sensor model derived from an equivalent circuit model (ECM). We propose design measures for both the sensing coil and the target, focusing on material selection to handle the impact of temperature variations. A key part of improving performance under varying temperatures includes model-based temperature compensation for the inductance of the sensing coil. We introduce a method to calibrate the sensor for large displacements, using a modified coupling coefficient based on field simulation data. Our analysis shows that this single-coil ECDS design maintains a position error of less than 0.2% full-scale for a temperature variation of 100 K for the sensing coil and 110 K for the target.

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.

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.

Where Did SARS-CoV-2 Come From?

Identifying the origin of SARS-CoV-2, the etiological agent of the current COVID-19 pandemic, may help us to avoid future epidemics of coronavirus and other zoonoses. Several theories about the zoonotic origin of SARS-CoV-2 have recently been proposed. Although Betacoronavirus found in Rhinolophus bats from China have been broadly implicated, their genetic dissimilarity to SARS-CoV-2 is so high that they are highly unlikely to be its direct ancestors. Thus, an intermediary host is suspected to link bat to human coronaviruses. Based on genomic CpG dinucleotide patterns in different coronaviruses from different hosts, it was suggested that SARS-CoV-2 might have evolved in a canid gastrointestinal tract prior to transmission to humans. However, similar CpG patterns are now reported in coronaviruses from other hosts, including bats themselves and pangolins. Therefore, reduced genomic CpG alone is not a highly predictive biomarker, suggesting a need for additional biomarkers to reveal intermediate hosts or tissues. The hunt for the zoonotic origin of SARS-CoV-2 continues.

Inferring transmission heterogeneity using virus genealogies: Estimation and targeted prevention.

Spread of HIV typically involves uneven transmission patterns where some individuals spread to a large number of individuals while others to only a few or none. Such transmission heterogeneity can impact how fast and how much an epidemic spreads. Further, more efficient interventions may be achieved by taking such transmission heterogeneity into account. To address these issues, we developed two phylogenetic methods based on virus sequence data: 1) to generally detect if significant transmission heterogeneity is present, and 2) to pinpoint where in a phylogeny high-level spread is occurring. We derive inference procedures to estimate model parameters, including the amount of transmission heterogeneity, in a sampled epidemic. We show that it is possible to detect transmission heterogeneity under a wide range of simulated situations, including incomplete sampling, varying levels of heterogeneity, and including within-host genetic diversity. When evaluating real HIV-1 data from different epidemic scenarios, we found a lower level of transmission heterogeneity in slowly spreading situations and a higher level of heterogeneity in data that included a rapid outbreak, while R0 and Sackin's index (overall tree shape statistic) were similar in the two scenarios, suggesting that our new method is able to detect transmission heterogeneity in real data. We then show by simulations that targeted prevention, where we pinpoint high-level spread using a coalescence measurement, is efficient when sequence data are collected in an ongoing surveillance system. Such phylogeny-guided prevention is efficient under both single-step contact tracing as well as iterative contact tracing as compared to random intervention.

Addressing Ethical Challenges in US-Based HIV Phylogenetic Research.

In recent years, phylogenetic analysis of HIV sequence data has been used in research studies to investigate transmission patterns between individuals and groups, including analysis of data from HIV prevention clinical trials, in molecular epidemiology, and in public health surveillance programs. Phylogenetic analysis can provide valuable information to inform HIV prevention efforts, but it also has risks, including stigma and marginalization of groups, or potential identification of HIV transmission between individuals. In response to these concerns, an interdisciplinary working group was assembled to address ethical challenges in US-based HIV phylogenetic research. The working group developed recommendations regarding (1) study design; (2) data security, access, and sharing; (3) legal issues; (4) community engagement; and (5) communication and dissemination. The working group also identified areas for future research and scholarship to promote ethical conduct of HIV phylogenetic research.

The Puzzle of HIV Neutral and Selective Evolution.

HIV is one of the fastest evolving organisms known. It evolves about 1 million times faster than its host, humans. Because HIV establishes chronic infections, with continuous evolution, its divergence within a single infected human surpasses the divergence of the entire humanoid history. Yet, it is still the same virus, infecting the same cell types and using the same replication machinery year after year. Hence, one would think that most mutations that HIV accumulates are neutral. But the picture is more complicated than that. HIV evolution is also a clear example of strong positive selection, that is, mutants have a survival advantage. How do these facts come together?

Phylogenetically resolving epidemiologic linkage.

Although the use of phylogenetic trees in epidemiological investigations has become commonplace, their epidemiological interpretation has not been systematically evaluated. Here, we use an HIV-1 within-host coalescent model to probabilistically evaluate transmission histories of two epidemiologically linked hosts. Previous critique of phylogenetic reconstruction has claimed that direction of transmission is difficult to infer, and that the existence of unsampled intermediary links or common sources can never be excluded. The phylogenetic relationship between the HIV populations of epidemiologically linked hosts can be classified into six types of trees, based on cladistic relationships and whether the reconstruction is consistent with the true transmission history or not. We show that the direction of transmission and whether unsampled intermediary links or common sources existed make very different predictions about expected phylogenetic relationships: (i) Direction of transmission can often be established when paraphyly exists, (ii) intermediary links can be excluded when multiple lineages were transmitted, and (iii) when the sampled individuals' HIV populations both are monophyletic a common source was likely the origin. Inconsistent results, suggesting the wrong transmission direction, were generally rare. In addition, the expected tree topology also depends on the number of transmitted lineages, the sample size, the time of the sample relative to transmission, and how fast the diversity increases after infection. Typically, 20 or more sequences per subject give robust results. We confirm our theoretical evaluations with analyses of real transmission histories and discuss how our findings should aid in interpreting phylogenetic results.

Phylodynamic Inference across Epidemic Scales.

Within-host genetic diversity and large transmission bottlenecks confound phylodynamic inference of epidemiological dynamics. Conventional phylodynamic approaches assume that nodes in a time-scaled pathogen phylogeny correspond closely to the time of transmission between hosts that are ancestral to the sample. However, when hosts harbor diverse pathogen populations, node times can substantially pre-date infection times. Imperfect bottlenecks can cause lineages sampled in different individuals to coalesce in unexpected patterns. To address realistic violations of standard phylodynamic assumptions we developed a new inference approach based on a multi-scale coalescent model, accounting for nonlinear epidemiological dynamics, heterogeneous sampling through time, non-negligible genetic diversity of pathogens within hosts, and imperfect transmission bottlenecks. We apply this method to HIV-1 and Ebola virus (EBOV) outbreak sequence data, illustrating how and when conventional phylodynamic inference may give misleading results. Within-host diversity of HIV-1 causes substantial upwards bias in the number of infected hosts using conventional coalescent models, but estimates using the multi-scale model have greater consistency with reported number of diagnoses through time. In contrast, we find that within-host diversity of EBOV has little influence on estimated numbers of infected hosts or reproduction numbers, and estimates are highly consistent with the reported number of diagnoses through time. The multi-scale coalescent also enables estimation of within-host effective population size using single sequences from a random sample of patients. We find within-host population genetic diversity of HIV-1 p17 to be 2Nµ=0.012 (95% CI 0.0066-0.023), which is lower than estimates based on HIV envelope serial sequencing of individual patients.

Impact of the HIV-1 genetic background and HIV-1 population size on the evolution of raltegravir resistance.

BACKGROUND: Emergence of resistance against integrase inhibitor raltegravir in human immunodeficiency virus type 1 (HIV-1) patients is generally associated with selection of one of three signature mutations: Y143C/R, Q148K/H/R or N155H, representing three distinct resistance pathways. The mechanisms that drive selection of a specific pathway are still poorly understood. We investigated the impact of the HIV-1 genetic background and population dynamics on the emergence of raltegravir resistance. Using deep sequencing we analyzed the integrase coding sequence (CDS) in longitudinal samples from five patients who initiated raltegravir plus optimized background therapy at viral loads > 5000 copies/ml. To investigate the role of the HIV-1 genetic background we created recombinant viruses containing the viral integrase coding region from pre-raltegravir samples from two patients in whom raltegravir resistance developed through different pathways. The in vitro selections performed with these recombinant viruses were designed to mimic natural population bottlenecks. RESULTS: Deep sequencing analysis of the viral integrase CDS revealed that the virological response to raltegravir containing therapy inversely correlated with the relative amount of unique sequence variants that emerged suggesting diversifying selection during drug pressure. In 4/5 patients multiple signature mutations representing different resistance pathways were observed. Interestingly, the resistant population can consist of a single resistant variant that completely dominates the population but also of multiple variants from different resistance pathways that coexist in the viral population. We also found evidence for increased diversification after stronger bottlenecks. In vitro selections with low viral titers, mimicking population bottlenecks, revealed that both recombinant viruses and HXB2 reference virus were able to select mutations from different resistance pathways, although typically only one resistance pathway emerged in each individual culture. CONCLUSIONS: The generation of a specific raltegravir resistant variant is not predisposed in the genetic background of the viral integrase CDS. Typically, in the early phases of therapy failure the sequence space is explored and multiple resistance pathways emerge and then compete for dominance which frequently results in a switch of the dominant population over time towards the fittest variant or even multiple variants of similar fitness that can coexist in the viral population.

Inference of Transmission Network Structure from HIV Phylogenetic Trees.

Phylogenetic inference is an attractive means to reconstruct transmission histories and epidemics. However, there is not a perfect correspondence between transmission history and virus phylogeny. Both node height and topological differences may occur, depending on the interaction between within-host evolutionary dynamics and between-host transmission patterns. To investigate these interactions, we added a within-host evolutionary model in epidemiological simulations and examined if the resulting phylogeny could recover different types of contact networks. To further improve realism, we also introduced patient-specific differences in infectivity across disease stages, and on the epidemic level we considered incomplete sampling and the age of the epidemic. Second, we implemented an inference method based on approximate Bayesian computation (ABC) to discriminate among three well-studied network models and jointly estimate both network parameters and key epidemiological quantities such as the infection rate. Our ABC framework used both topological and distance-based tree statistics for comparison between simulated and observed trees. Overall, our simulations showed that a virus time-scaled phylogeny (genealogy) may be substantially different from the between-host transmission tree. This has important implications for the interpretation of what a phylogeny reveals about the underlying epidemic contact network. In particular, we found that while the within-host evolutionary process obscures the transmission tree, the diversification process and infectivity dynamics also add discriminatory power to differentiate between different types of contact networks. We also found that the possibility to differentiate contact networks depends on how far an epidemic has progressed, where distance-based tree statistics have more power early in an epidemic. Finally, we applied our ABC inference on two different outbreaks from the Swedish HIV-1 epidemic.

PrimerDesign-M: a multiple-alignment based multiple-primer design tool for walking across variable genomes.

SUMMARY: Analyses of entire viral genomes or mtDNA requires comprehensive design of many primers across their genomes. Furthermore, simultaneous optimization of several DNA primer design criteria may improve overall experimental efficiency and downstream bioinformatic processing. To achieve these goals, we developed PrimerDesign-M. It includes several options for multiple-primer design, allowing researchers to efficiently design walking primers that cover long DNA targets, such as entire HIV-1 genomes, and that optimizes primers simultaneously informed by genetic diversity in multiple alignments and experimental design constraints given by the user. PrimerDesign-M can also design primers that include DNA barcodes and minimize primer dimerization. PrimerDesign-M finds optimal primers for highly variable DNA targets and facilitates design flexibility by suggesting alternative designs to adapt to experimental conditions. AVAILABILITY AND IMPLEMENTATION: PrimerDesign-M is available as a webtool at http://www.hiv.lanl.gov/content/sequence/PRIMER_DESIGN/primer_design.html CONTACT: tkl@lanl.gov or seq-info@lanl.gov.

Recombination Enhances HIV-1 Envelope Diversity by Facilitating the Survival of Latent Genomic Fragments in the Plasma Virus Population.

HIV-1 is subject to immune pressure exerted by the host, giving variants that escape the immune response an advantage. Virus released from activated latent cells competes against variants that have continually evolved and adapted to host immune pressure. Nevertheless, there is increasing evidence that virus displaying a signal of latency survives in patient plasma despite having reduced fitness due to long-term immune memory. We investigated the survival of virus with latent envelope genomic fragments by simulating within-host HIV-1 sequence evolution and the cycling of viral lineages in and out of the latent reservoir. Our model incorporates a detailed mutation process including nucleotide substitution, recombination, latent reservoir dynamics, diversifying selection pressure driven by the immune response, and purifying selection pressure asserted by deleterious mutations. We evaluated the ability of our model to capture sequence evolution in vivo by comparing our simulated sequences to HIV-1 envelope sequence data from 16 HIV-infected untreated patients. Empirical sequence divergence and diversity measures were qualitatively and quantitatively similar to those of our simulated HIV-1 populations, suggesting that our model invokes realistic trends of HIV-1 genetic evolution. Moreover, reconstructed phylogenies of simulated and patient HIV-1 populations showed similar topological structures. Our simulation results suggest that recombination is a key mechanism facilitating the persistence of virus with latent envelope genomic fragments in the productively infected cell population. Recombination increased the survival probability of latent virus forms approximately 13-fold. Prevalence of virus with latent fragments in productively infected cells was observed in only 2% of simulations when we ignored recombination, while the proportion increased to 27% of simulations when we allowed recombination. We also found that the selection pressures exerted by different fitness landscapes influenced the shape of phylogenies, diversity trends, and survival of virus with latent genomic fragments. Our model predicts that the persistence of latent genomic fragments from multiple different ancestral origins increases sequence diversity in plasma for reasonable fitness landscapes.

Timing and order of transmission events is not directly reflected in a pathogen phylogeny.

Pathogen phylogenies are often used to infer spread among hosts. There is, however, not an exact match between the pathogen phylogeny and the host transmission history. Here, we examine in detail the limitations of this relationship. First, all splits in a pathogen phylogeny of more than 1 host occur within hosts, not at the moment of transmission, predating the transmission events as described by the pretransmission interval. Second, the order in which nodes in a phylogeny occur may be reflective of the within-host dynamics rather than epidemiologic relationships. To investigate these phenomena, motivated by within-host diversity patterns, we developed a two-phase coalescent model that includes a transmission bottleneck followed by linear outgrowth to a maximum population size followed by either stabilization or decline of the population. The model predicts that the pretransmission interval shrinks compared with predictions based on constant population size or a simple transmission bottleneck. Because lineages coalesce faster in a small population, the probability of a pathogen phylogeny to resemble the transmission history depends on when after infection a donor transmits to a new host. We also show that the probability of inferring the incorrect order of multiple transmissions from the same host is high. Finally, we compare time of HIV-1 infection informed by genetic distances in phylogenies to independent biomarker data, and show that, indeed, the pretransmission interval biases phylogeny-based estimates of when transmissions occurred. We describe situations where caution is needed not to misinterpret which parts of a phylogeny that may indicate outbreaks and tight transmission clusters.

Recombination elevates the effective evolutionary rate and facilitates the establishment of HIV-1 infection in infants after mother-to-child transmission.

BACKGROUND: Previous studies have demonstrated that single HIV-1 genotypes are commonly transmitted from mother to child, but such analyses primarily used single samples from mother and child. It is possible that in a single sample, obtained early after infection, only the most replication competent virus is detected even when other forms may have been transmitted. Such forms may have advantages later in infection, and may thus be detected in follow-up samples. Because HIV-1 frequently recombines, phylogenetic analyses that ignore recombination may miss transmission of multiple forms if they recombine after transmission. Moreover, recombination may facilitate adaptation, thus providing an advantage in establishing infection. The effect of recombination on viral evolution in HIV-1 infected children has not been well defined. RESULTS: We analyzed full-length env sequences after single genome amplification from the plasma of four subtype B HIV-1 infected women (11-67 env clones from 1 time point within a month prior to delivery) and their non-breastfed, intrapartum-infected children (3-6 longitudinal time points per child starting at the time of HIV-1 diagnosis). To address the potential beneficial or detrimental effects of recombination, we used a recently developed hierarchical recombination detection method based on the pairwise homoplasy index (PHI)-test. Recombination was observed in 9-67% of the maternal sequences and in 25-60% of the child sequences. In the child, recombination only occurred between variants that had evolved after transmission; taking recombination into account, we identified transmission of only 1 or 2 phylogenetic lineages from mother to child. Effective HIV-1 evolutionary rates of HIV-1 were initially high in the child and slowed over time (after 1000 days). Recombination was associated with elevated evolutionary rates. CONCLUSIONS: Our results confirm that 1-2 variants are typically transmitted from mothers to their newborns. They also demonstrate that early abundant recombination elevates the effective evolutionary rate, suggesting that recombination increases the rate of adaptation in HIV-1 evolution.

Reduced evolutionary rates in HIV-1 reveal extensive latency periods among replicating lineages.

BACKGROUND: HIV-1 can persist for the duration of a patient's life due in part to its ability to hide from the immune system, and from antiretroviral drugs, in long-lived latent reservoirs. Latent forms of HIV-1 may also be disproportionally involved in transmission. Thus, it is important to detect and quantify latency in the HIV-1 life cycle. RESULTS: We developed a novel molecular clock-based phylogenetic tool to investigate the prevalence of HIV-1 lineages that have experienced latency. The method removes alternative sources that may affect evolutionary rates, such as hypermutation, recombination, and selection, to reveal the contribution of generation-time effects caused by latency. Our method was able to recover latent lineages with high specificity and sensitivity, and low false discovery rates, even on relatively short branches on simulated phylogenies. Applying the tool to HIV-1 sequences from 26 patients, we show that the majority of phylogenetic lineages have been affected by generation-time effects in every patient type, whether untreated, elite controller, or under effective or failing treatment. Furthermore, we discovered extensive effects of latency in sequence data (gag, pol, and env) from reservoirs as well as in the replicating plasma population. To better understand our phylogenetic findings, we developed a dynamic model of virus-host interactions to investigate the proportion of lineages in the actively replicating population that have ever been latent. Assuming neutral evolution, our dynamic modeling showed that under most parameter conditions, it is possible for a few activated latent viruses to propagate so that in time, most HIV-1 lineages will have been latent at some time in their past. CONCLUSIONS: These results suggest that cycling in and out of latency plays a major role in the evolution of HIV-1. Thus, no aspect of HIV-1 evolution can be fully understood without considering latency - including treatment, drug resistance, immune evasion, transmission, and pathogenesis.