Divergence dating using mixed effects clock modelling: An application to HIV-1

Mapping Transmission Dynamics and Drug Resistance Surveillance in the Cyprus HIV-1 Epidemic (2017-2021)

The human immunodeficiency virus type 1 (HIV-1) epidemic has been a major public health threat on a global scale since the early 1980s. Despite the introduction of combination antiretroviral therapy (cART), the incidence of new HIV-1 infections continues to rise in some regions around the world. Thus, with the continuous transmission of HIV-1 and the lack of a cure, it is imperative for molecular epidemiological studies to be performed, to monitor the infection and ultimately be able to control the spread of this virus. This work provides a comprehensive molecular epidemiological analysis of the HIV-1 infection in Cyprus, through examining 305 HIV-1 sequences collected between 9 March 2017 and 14 October 2021. Employing advanced statistical and bioinformatic techniques, the research delved deeply into understanding the transmission dynamics of the HIV-1 epidemic in Cyprus, as well as the monitoring of HIV-1's genetic diversity and the surveillance of transmitted drug resistance. The characterization of Cyprus's HIV-1 epidemic revealed a diverse landscape, comprising 21 HIV-1 group M pure subtypes and circulating recombinant forms (CRFs), alongside numerous uncharacterized recombinant strains. Subtypes A1 and B emerged as the most prevalent strains, followed by CRF02_AG. The findings of this study also revealed high levels of transmitted drug resistance (TDR) patterns, raising concerns for the efficacy of cART. The demographic profiles of individuals involved in HIV-1 transmission underscored the disproportionate burden borne by young to middle-aged Cypriot males, particularly those in the MSM community, who reported contracting the virus in Cyprus. An assessment of the spatiotemporal evolutionary dynamics illustrated the global interconnectedness of HIV-1 transmission networks, implicating five continents in the dissemination of strains within Cyprus: Europe, Africa, Asia, North America, and Oceania. Overall, this study advances the comprehension of the HIV-1 epidemic in Cyprus and highlights the importance of understanding HIV-1's transmission dynamics through continuous surveillance efforts. Furthermore, this work emphasizes the critical role of state-of-the-art bioinformatics analyses in addressing the challenges posed by HIV-1 transmission globally, laying the groundwork for public health interventions aimed at curbing its spread and improving patient outcomes.

Modeling Substitution Rate Evolution across Lineages and Relaxing the Molecular Clock

Relaxing the molecular clock using models of how substitution rates change across lineages has become essential for addressing evolutionary problems. The diversity of rate evolution models and their implementations are substantial, and studies have demonstrated their impact on divergence time estimates can be as significant as that of calibration information. In this review, we trace the development of rate evolution models from the proposal of the molecular clock concept to the development of sophisticated Bayesian and non-Bayesian methods that handle rate variation in phylogenies. We discuss the various approaches to modeling rate evolution, provide a comprehensive list of available software, and examine the challenges and advancements of the prevalent Bayesian framework, contrasting them to faster non-Bayesian methods. Lastly, we offer insights into potential advancements in the field in the era of big data.

Changing selection on amino acid substitutions in Gag protein between major HIV-1 subtypes

Amino acid preferences at a protein site depend on the role of this site in protein function and structure as well as on external constraints. All these factors can change in the course of evolution, making amino acid propensities of a site time-dependent. When viral subtypes divergently evolve in different host subpopulations, such changes may depend on genetic, medical, and sociocultural differences between these subpopulations. Here, using our previously developed phylogenetic approach, we describe sixty-nine amino acid sites of the Gag protein of human immunodeficiency virus type 1 (HIV-1) where amino acids have different impact on viral fitness in six major subtypes of the type M. These changes in preferences trigger adaptive evolution; indeed, 32 (46 per cent) of these sites experienced strong positive selection at least in one of the subtypes. At some of the sites, changes in amino acid preferences may be associated with differences in immune escape between subtypes. The prevalence of an amino acid in a protein site within a subtype is only a poor predictor for whether this amino acid is preferred in this subtype according to the phylogenetic analysis. Therefore, attempts to identify the factors of viral evolution from comparative genomics data should integrate across multiple sources of information.

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.

Shrinkage-based Random Local Clocks with Scalable Inference

Molecular clock models undergird modern methods of divergence-time estimation. Local clock models propose that the rate of molecular evolution is constant within phylogenetic subtrees. Current local clock inference procedures exhibit one or more weaknesses, namely they achieve limited scalability to trees with large numbers of taxa, impose model misspecification, or require a priori knowledge of the existence and location of clocks. To overcome these challenges, we present an autocorrelated, Bayesian model of heritable clock rate evolution that leverages heavy-tailed priors with mean zero to shrink increments of change between branch-specific clocks. We further develop an efficient Hamiltonian Monte Carlo sampler that exploits closed form gradient computations to scale our model to large trees. Inference under our shrinkage clock exhibits a speed-up compared to the popular random local clock when estimating branch-specific clock rates on a variety of simulated datasets. This speed-up increases with the size of the problem. We further show our shrinkage clock recovers known local clocks within a rodent and mammalian phylogeny. Finally, in a problem that once appeared computationally impractical, we investigate the heritable clock structure of various surface glycoproteins of influenza A virus in the absence of prior knowledge about clock placement. We implement our shrinkage clock and make it publicly available in the BEAST software package.

Scalable Bayesian Divergence Time Estimation With Ratio Transformations

Divergence time estimation is crucial to provide temporal signals for dating biologically important events from species divergence to viral transmissions in space and time. With the advent of high-throughput sequencing, recent Bayesian phylogenetic studies have analyzed hundreds to thousands of sequences. Such large-scale analyses challenge divergence time reconstruction by requiring inference on highly correlated internal node heights that often become computationally infeasible. To overcome this limitation, we explore a ratio transformation that maps the original $N-1$ internal node heights into a space of one height parameter and $N-2$ ratio parameters. To make the analyses scalable, we develop a collection of linear-time algorithms to compute the gradient and Jacobian-associated terms of the log-likelihood with respect to these ratios. We then apply Hamiltonian Monte Carlo sampling with the ratio transform in a Bayesian framework to learn the divergence times in 4 pathogenic viruses (West Nile virus, rabies virus, Lassa virus, and Ebola virus) and the coralline red algae. Our method both resolves a mixing issue in the West Nile virus example and improves inference efficiency by at least 5-fold for the Lassa and rabies virus examples as well as for the algae example. Our method now also makes it computationally feasible to incorporate mixed-effects molecular clock models for the Ebola virus example, confirms the findings from the original study, and reveals clearer multimodal distributions of the divergence times of some clades of interest.

Newly identified lineages of porcine hemagglutinating encephalomyelitis virus exhibit respiratory phenotype

Swine pathogens have a long history of zoonotic transmission to humans, occasionally leading to sustained outbreaks or pandemics. Through a retrospective epidemiological study of swine populations in China, we describe novel lineages of porcine hemagglutinating encephalomyelitis virus (PHEV) complex coronaviruses (CoVs) that cause exclusively respiratory symptoms with no signs of the neurological symptoms typically associated with classical PHEV infection. Through large-scale epidemiological surveillance, we show that these novel lineages have circulated in at least eight provinces in southeastern China. Phylogenetic and recombination analyses of twenty-four genomes identified two major viral lineages causing respiratory symptoms with extensive recombination within them, between them, and between classical PHEV and the novel respiratory variant PHEV (rvPHEV) lineages. Divergence times among the sampled lineages in the PHEV virus complex date back to 1886-1958 (mean estimate 1928), with the two major rvPHEV lineages separating approximately 20 years later. Many rvPHEV viruses show amino acid substitutions at the carbohydrate-binding site of hemagglutinin esterase (HE) and/or have lost the cysteine required for HE dimerization. This resembles the early adaptation of human CoVs, where HE lost its hemagglutination ability to adapt to growth in the human respiratory tract. Our study represents the first report of the evolutionary history of rvPHEV circulating in swine and highlights the importance of characterizing CoV diversity and recombination in swine to identify pathogens with outbreak potential that could threaten swine farming.

Phylogenetic Reconstruction and Functional Characterization of the Ancestral Nef Protein of Primate Lentiviruses

Nef is an accessory protein unique to the primate HIV-1, HIV-2, and SIV lentiviruses. During infection, Nef functions by interacting with multiple host proteins within infected cells to evade the immune response and enhance virion infectivity. Notably, Nef can counter immune regulators such as CD4 and MHC-I, as well as the SERINC5 restriction factor in infected cells. In this study, we generated a posterior sample of time-scaled phylogenies relating SIV and HIV Nef sequences, followed by reconstruction of ancestral sequences at the root and internal nodes of the sampled trees up to the HIV-1 Group M ancestor. Upon expression of the ancestral primate lentivirus Nef protein within CD4+ HeLa cells, flow cytometry analysis revealed that the primate lentivirus Nef ancestor robustly downregulated cell-surface SERINC5, yet only partially downregulated CD4 from the cell surface. Further analysis revealed that the Nef-mediated CD4 downregulation ability evolved gradually, while Nef-mediated SERINC5 downregulation was recovered abruptly in the HIV-1/M ancestor. Overall, this study provides a framework to reconstruct ancestral viral proteins and enable the functional characterization of these proteins to delineate how functions could have changed throughout evolutionary history.

Modeling Drug Resistance Emergence and Transmission in HIV-1 in the UK

A deeper understanding of HIV-1 transmission and drug resistance mechanisms can lead to improvements in current treatment policies. However, the rates at which HIV-1 drug resistance mutations (DRMs) are acquired and which transmitted DRMs persist are multi-factorial and vary considerably between different mutations. We develop a method for the estimation of drug resistance acquisition and transmission patterns. The method uses maximum likelihood ancestral character reconstruction informed by treatment roll-out dates and allows for the analysis of very large datasets. We apply our method to transmission trees reconstructed on the data obtained from the UK HIV Drug Resistance Database to make predictions for known DRMs. Our results show important differences between DRMs, in particular between polymorphic and non-polymorphic DRMs and between the B and C subtypes. Our estimates of reversion times, based on a very large number of sequences, are compatible but more accurate than those already available in the literature, with narrower confidence intervals. We consistently find that large resistance clusters are associated with polymorphic DRMs and DRMs with long loss times, which require special surveillance. As in other high-income countries (e.g., Switzerland), the prevalence of sequences with DRMs is decreasing, but among these, the fraction of transmitted resistance is clearly increasing compared to the fraction of acquired resistance mutations. All this indicates that efforts to monitor these mutations and the emergence of resistance clusters in the population must be maintained in the long term.

Women in the European Virus Bioinformatics Center

Viruses are the cause of a considerable burden to human, animal and plant health, while on the other hand playing an important role in regulating entire ecosystems. The power of new sequencing technologies combined with new tools for processing "Big Data" offers unprecedented opportunities to answer fundamental questions in virology. Virologists have an urgent need for virus-specific bioinformatics tools. These developments have led to the formation of the European Virus Bioinformatics Center, a network of experts in virology and bioinformatics who are joining forces to enable extensive exchange and collaboration between these research areas. The EVBC strives to provide talented researchers with a supportive environment free of gender bias, but the gender gap in science, especially in math-intensive fields such as computer science, persists. To bring more talented women into research and keep them there, we need to highlight role models to spark their interest, and we need to ensure that female scientists are not kept at lower levels but are given the opportunity to lead the field. Here we showcase the work of the EVBC and highlight the achievements of some outstanding women experts in virology and viral bioinformatics.

Molecular Epidemiology of Porcine Parvovirus Type 1 (PPV1) and the Reactivity of Vaccine-Induced Antisera Against Historical and Current PPV1 Strains

Porcine Parvovirus Type 1 (PPV1) contributes to important losses in the swine industry worldwide. During a PPV1 infection, embryos and fetuses are targeted, resulting in stillbirth, mummification, embryonic death, and infertility (SMEDI syndrome). Even though vaccination is common in gilts and sows, strains mainly belonging to the 27a-like group have been spreading in Europe since early 2000s, resulting in SMEDI problems and requiring in-depth studies into the molecular epidemiology and vaccination efficacy of commercial vaccines. Here, we show that PPV1 has evolved since 1855 [1737, 1933] at a rate of 4.71 × 10⁻⁵ nucleotide substitutions per site per year. Extensive sequencing allowed evaluating and reassessing the current PPV1 VP1-based classifications, providing evidence for the existence of four relevant phylogenetic groups. While most European strains belong to the PPV1a (G1) or PPV1b (G2 or 27a-like) group, most Asian and American G2 strains and some European strains were divided into virulent PPV1c (e.g. NADL-8) and attenuated PPV1d (e.g. NADL-2) groups. The increase in the swine population, vaccination degree, and health management (vaccination and biosafety) influenced the spread of PPV1. The reactivity of anti-PPV1 antibodies from sows vaccinated with Porcilis^© Parvo, Eryseng^© Parvo, or ReproCyc^© ParvoFLEX against different PPV1 field strains was the highest upon vaccination with ReproCyc^© ParvoFLEX, followed by Eryseng^© Parvo, and Porcilis^© Parvo. Our findings contribute to the evaluation of the immunogenicity of existing vaccines and support the development of new vaccine candidates. Finally, the potential roles of cluster-specific hallmark amino acids in elevated pathogenicity and viral entry are discussed.

Newly Discovered Archival Data Show Coincidence of a Peak of Sexually Transmitted Diseases with the Early Epicenter of Pandemic HIV-1

To which extent STDs facilitated HIV-1 adaptation to humans, sparking the pandemic, is still unknown. We searched colonial medical records from 1906–1958 for Leopoldville, Belgian Congo, which was the initial epicenter of pandemic HIV-1, compiling counts of treated STD cases in both Africans and Europeans. Almost all Europeans were being treated, while for Africans, generalized treatment started only in 1929. Treated STD counts in Europeans thus reflect STD infection rates more accurately compared to counts in Africans. In Africans, the highest recorded STD treatment incidence was in 1929–1935, declining to low levels in the 1950s. In Europeans, the recorded treatment incidences were highest during the period 1910–1920, far exceeding those in Africans. Europeans were overwhelmingly male and had frequent sexual contact with African females. Consequently, high STD incidence among Europeans must have coincided with high prevalence and incidence in the city’s African population. The data strongly suggest the worst STD period was 1910–1920 for both Africans and Europeans, which coincides with the estimated origin of pandemic HIV-1. Given the strong effect of STD coinfections on HIV transmission, these new data support our hypothesis of a causal effect of STDs on the epidemic emergence of HIV-1.

Phylogenetic analysis of HIV-1 shows frequent cross-country transmission and local population expansions

Understanding of pandemics depends on the characterization of pathogen collections from well-defined and demographically diverse cohorts. Since its emergence in Congo almost a century ago, Human Immunodeficiency Virus Type 1 (HIV-1) has geographically spread and genetically diversified into distinct viral subtypes. Phylogenetic analysis can be used to reconstruct the ancestry of the virus to better understand the origin and distribution of subtypes. We sequenced two 3.6-kb amplicons of HIV-1 genomes from 3,197 participants in a clinical trial with consistent and uniform sampling at sites across 35 countries and analyzed our data with another 2,632 genomes that comprehensively reflect the HIV-1 genetic diversity. We used maximum likelihood phylogenetic analysis coupled with geographical information to infer the state of ancestors. The majority of our sequenced genomes (n = 2,501) were either pure subtypes (A-D, F, and G) or CRF01_AE. The diversity and distribution of subtypes across geographical regions differed; USA showed the most homogenous subtype population, whereas African samples were most diverse. We delineated transmission of the four most prevalent subtypes in our dataset (A, B, C, and CRF01_AE), and our results suggest both continuous and frequent transmission of HIV-1 over country borders, as well as single transmission events being the seed of endemic population expansions. Overall, we show that coupling of genetic and geographical information of HIV-1 can be used to understand the origin and spread of pandemic pathogens.

Genomics and epidemiology of the P.1 SARS-CoV-2 lineage in Manaus, Brazil

Cases of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection in Manaus, Brazil, resurged in late 2020 despite previously high levels of infection. Genome sequencing of viruses sampled in Manaus between November 2020 and January 2021 revealed the emergence and circulation of a novel SARS-CoV-2 variant of concern. Lineage P.1 acquired 17 mutations, including a trio in the spike protein (K417T, E484K, and N501Y) associated with increased binding to the human ACE2 (angiotensin-converting enzyme 2) receptor. Molecular clock analysis shows that P.1 emergence occurred around mid-November 2020 and was preceded by a period of faster molecular evolution. Using a two-category dynamical model that integrates genomic and mortality data, we estimate that P.1 may be 1.7- to 2.4-fold more transmissible and that previous (non-P.1) infection provides 54 to 79% of the protection against infection with P.1 that it provides against non-P.1 lineages. Enhanced global genomic surveillance of variants of concern, which may exhibit increased transmissibility and/or immune evasion, is critical to accelerate pandemic responsiveness.

Bayesian Evaluation of Temporal Signal in Measurably Evolving Populations

Phylogenetic methods can use the sampling times of molecular sequence data to calibrate the molecular clock, enabling the estimation of evolutionary rates and timescales for rapidly evolving pathogens and data sets containing ancient DNA samples. A key aspect of such calibrations is whether a sufficient amount of molecular evolution has occurred over the sampling time window, that is, whether the data can be treated as having come from a measurably evolving population. Here, we investigate the performance of a fully Bayesian evaluation of temporal signal (BETS) in sequence data. The method involves comparing the fit to the data of two models: a model in which the data are accompanied by the actual (heterochronous) sampling times, and a model in which the samples are constrained to be contemporaneous (isochronous). We conducted simulations under a wide range of conditions to demonstrate that BETS accurately classifies data sets according to whether they contain temporal signal or not, even when there is substantial among-lineage rate variation. We explore the behavior of this classification in analyses of five empirical data sets: modern samples of A/H1N1 influenza virus, the bacterium Bordetella pertussis, coronaviruses from mammalian hosts, ancient DNA from Hepatitis B virus, and mitochondrial genomes of dog species. Our results indicate that BETS is an effective alternative to other tests of temporal signal. In particular, this method has the key advantage of allowing a coherent assessment of the entire model, including the molecular clock and tree prior which are essential aspects of Bayesian phylodynamic analyses.

Ferreira AC, Schnekenberg RP, Laydon DJ, Walker PGT, Schlüter HM, dos Santos ALP, Vidal MS, Del Caro VS, Filho RMF, dos Santos HM, Aguiar RS, Modena JLP, Nelson B, Hay JA, Monod M, Miscouridou X, Coupland H, Sonabend R, Vollmer M, Gandy A, Suchard MA, Bowden TA, Pond SLK, Wu CH, Ratmann O, Ferguson NM, Dye C, Loman NJ, Lemey P, Rambaut A, Fraiji NA, Carvalho MDPSS, Pybus OG, Flaxman S, Bhatt S, Sabino EC. Genomics and epidemiology of a novel SARS-CoV-2 lineage in Manaus, Brazil

Cases of SARS-CoV-2 infection in Manaus, Brazil, resurged in late 2020, despite high levels of previous infection there. Through genome sequencing of viruses sampled in Manaus between November 2020 and January 2021, we identified the emergence and circulation of a novel SARS-CoV-2 variant of concern, lineage P.1, that acquired 17 mutations, including a trio in the spike protein (K417T, E484K and N501Y) associated with increased binding to the human ACE2 receptor. Molecular clock analysis shows that P.1 emergence occurred around early November 2020 and was preceded by a period of faster molecular evolution. Using a two-category dynamical model that integrates genomic and mortality data, we estimate that P.1 may be 1.4-2.2 times more transmissible and 25-61% more likely to evade protective immunity elicited by previous infection with non-P.1 lineages. Enhanced global genomic surveillance of variants of concern, which may exhibit increased transmissibility and/or immune evasion, is critical to accelerate pandemic responsiveness.

Gradients Do Grow on Trees: A Linear-Time O(N)-Dimensional Gradient for Statistical Phylogenetics

Calculation of the log-likelihood stands as the computational bottleneck for many statistical phylogenetic algorithms. Even worse is its gradient evaluation, often used to target regions of high probability. Order O(N)-dimensional gradient calculations based on the standard pruning algorithm require O(N2) operations, where N is the number of sampled molecular sequences. With the advent of high-throughput sequencing, recent phylogenetic studies have analyzed hundreds to thousands of sequences, with an apparent trend toward even larger data sets as a result of advancing technology. Such large-scale analyses challenge phylogenetic reconstruction by requiring inference on larger sets of process parameters to model the increasing data heterogeneity. To make these analyses tractable, we present a linear-time algorithm for O(N)-dimensional gradient evaluation and apply it to general continuous-time Markov processes of sequence substitution on a phylogenetic tree without a need to assume either stationarity or reversibility. We apply this approach to learn the branch-specific evolutionary rates of three pathogenic viruses: West Nile virus, Dengue virus, and Lassa virus. Our proposed algorithm significantly improves inference efficiency with a 126- to 234-fold increase in maximum-likelihood optimization and a 16- to 33-fold computational performance increase in a Bayesian framework.

A near full-length HIV-1 genome from 1966 recovered from formalin-fixed paraffin-embedded tissue

With very little direct biological data of HIV-1 from before the 1980s, far-reaching evolutionary and epidemiological inferences regarding the long prediscovery phase of this pandemic are based on extrapolations by phylodynamic models of HIV-1 genomic sequences gathered mostly over recent decades. Here, using a very sensitive multiplex RT-PCR assay, we screened 1,645 formalin-fixed paraffin-embedded tissue specimens collected for pathology diagnostics in Central Africa between 1958 and 1966. We report the near-complete viral genome in one HIV-1 positive specimen from Kinshasa, Democratic Republic of Congo (DRC), from 1966 ("DRC66")-a nonrecombinant sister lineage to subtype C that constitutes the oldest HIV-1 near full-length genome recovered to date. Root-to-tip plots showed the DRC66 sequence is not an outlier as would be expected if dating estimates from more recent genomes were systematically biased; and inclusion of the DRC66 sequence in tip-dated BEAST analyses did not significantly alter root and internal node age estimates based on post-1978 HIV-1 sequences. There was larger variation in divergence time estimates among datasets that were subsamples of the available HIV-1 genomes from 1978 to 2014, showing the inherent phylogenetic stochasticity across subsets of the real HIV-1 diversity. Our phylogenetic analyses date the origin of the pandemic lineage of HIV-1 to a time period around the turn of the 20th century (1881 to 1918). In conclusion, this unique archival HIV-1 sequence provides direct genomic insight into HIV-1 in 1960s DRC, and, as an ancient-DNA calibrator, it validates our understanding of HIV-1 evolutionary history.