Addressing pandemic-wide systematic errors in the SARS-CoV-2 phylogeny.

The SARS-CoV-2 genome occupies a unique place in infection biology - it is the most highly sequenced genome on earth (making up over 20% of public sequencing datasets) with fine scale information on sampling date and geography, and has been subject to unprecedented intense analysis. As a result, these phylogenetic data are an incredibly valuable resource for science and public health. However, the vast majority of the data was sequenced by tiling amplicons across the full genome, with amplicon schemes that changed over the pandemic as mutations in the viral genome interacted with primer binding sites. In combination with the disparate set of genome assembly workflows and lack of consistent quality control (QC) processes, the current genomes have many systematic errors that have evolved with the virus and amplicon schemes. These errors have significant impacts on the phylogeny, and therefore over the last few years, many thousands of hours of researchers time has been spent in "eyeballing" trees, looking for artefacts, and then patching the tree. Given the huge value of this dataset, we therefore set out to reprocess the complete set of public raw sequence data in a rigorous amplicon-aware manner, and build a cleaner phylogeny. Here we provide a global tree of 3,960,704 samples, built from a consistently assembled set of high quality consensus sequences from all available public data as of March 2023, viewable at https://viridian.taxonium.org . Each genome was constructed using a novel assembly tool called Viridian ( https://github.com/iqbal-lab-org/viridian ), developed specifically to process amplicon sequence data, eliminating artefactual errors and mask the genome at low quality positions. We provide simulation and empirical validation of the methodology, and quantify the improvement in the phylogeny. Phase 2 of our project will address the fact that the data in the public archives is heavily geographically biased towards the Global North. We therefore have contributed new raw data to ENA/SRA from many countries including Ghana, Thailand, Laos, Sri Lanka, India, Argentina and Singapore. We will incorporate these, along with all public raw data submitted between March 2023 and the current day, into an updated set of assemblies, and phylogeny. We hope the tree, consensus sequences and Viridian will be a valuable resource for researchers.

Quantifying how single dose Ad26.COV2.S vaccine efficacy depends on Spike sequence features.

In the ENSEMBLE randomized, placebo-controlled phase 3 trial (NCT04505722), estimated single-dose Ad26.COV2.S vaccine efficacy (VE) was 56% against moderate to severe-critical COVID-19. SARS-CoV-2 Spike sequences were determined from 484 vaccine and 1,067 placebo recipients who acquired COVID-19. In this set of prespecified analyses, we show that in Latin America, VE was significantly lower against Lambda vs. Reference and against Lambda vs. non-Lambda [family-wise error rate (FWER) p < 0.05]. VE differed by residue match vs. mismatch to the vaccine-insert at 16 amino acid positions (4 FWER p < 0.05; 12 q-value ≤ 0.20); significantly decreased with physicochemical-weighted Hamming distance to the vaccine-strain sequence for Spike, receptor-binding domain, N-terminal domain, and S1 (FWER p < 0.001); differed (FWER ≤ 0.05) by distance to the vaccine strain measured by 9 antibody-epitope escape scores and 4 NTD neutralization-impacting features; and decreased (p = 0.011) with neutralization resistance level to vaccinee sera. VE against severe-critical COVID-19 was stable across most sequence features but lower against the most distant viruses.

US National Institutes of Health Prioritization of SARS-CoV-2 Variants.

Since late 2020, SARS-CoV-2 variants have regularly emerged with competitive and phenotypic differences from previously circulating strains, sometimes with the potential to escape from immunity produced by prior exposure and infection. The Early Detection group is one of the constituent groups of the US National Institutes of Health National Institute of Allergy and Infectious Diseases SARS-CoV-2 Assessment of Viral Evolution program. The group uses bioinformatic methods to monitor the emergence, spread, and potential phenotypic properties of emerging and circulating strains to identify the most relevant variants for experimental groups within the program to phenotypically characterize. Since April 2021, the group has prioritized variants monthly. Prioritization successes include rapidly identifying most major variants of SARS-CoV-2 and providing experimental groups within the National Institutes of Health program easy access to regularly updated information on the recent evolution and epidemiology of SARS-CoV-2 that can be used to guide phenotypic investigations.

Defining the risk of SARS-CoV-2 variants on immune protection.

The global emergence of many severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants jeopardizes the protective antiviral immunity induced after infection or vaccination. To address the public health threat caused by the increasing SARS-CoV-2 genomic diversity, the National Institute of Allergy and Infectious Diseases within the National Institutes of Health established the SARS-CoV-2 Assessment of Viral Evolution (SAVE) programme. This effort was designed to provide a real-time risk assessment of SARS-CoV-2 variants that could potentially affect the transmission, virulence, and resistance to infection- and vaccine-induced immunity. The SAVE programme is a critical data-generating component of the US Government SARS-CoV-2 Interagency Group to assess implications of SARS-CoV-2 variants on diagnostics, vaccines and therapeutics, and for communicating public health risk. Here we describe the coordinated approach used to identify and curate data about emerging variants, their impact on immunity and effects on vaccine protection using animal models. We report the development of reagents, methodologies, models and notable findings facilitated by this collaborative approach and identify future challenges. This programme is a template for the response to rapidly evolving pathogens with pandemic potential by monitoring viral evolution in the human population to identify variants that could reduce the effectiveness of countermeasures.

HIV-1 infections with multiple founders associate with the development of neutralization breadth.

Eliciting broadly neutralizing antibodies (bnAbs) is a cornerstone of HIV-1 vaccine strategies. Comparing HIV-1 envelope (env) sequences from the first weeks of infection to the breadth of antibody responses observed several years after infection can help define viral features critical to vaccine design. We investigated the relationship between HIV-1 env genetics and the development of neutralization breadth in 70 individuals enrolled in a prospective acute HIV-1 cohort. Half of the individuals who developed bnAbs were infected with multiple HIV-1 founder variants, whereas all individuals with limited neutralization breadth had been infected with single HIV-1 founders. Accordingly, at HIV-1 diagnosis, env diversity was significantly higher in participants who later developed bnAbs compared to those with limited breadth (p = 0.012). This association between founder multiplicity and the subsequent development of neutralization breadth was also observed in 56 placebo recipients in the RV144 vaccine efficacy trial. In addition, we found no evidence that neutralization breath was heritable when analyzing env sequences from the 126 participants. These results demonstrate that the presence of slightly different HIV-1 variants in acute infection could promote the induction of bnAbs, suggesting a novel vaccine strategy, whereby an initial immunization with a cocktail of minimally distant antigens would be able to initiate bnAb development towards breadth.

Timing HIV infection with a simple and accurate population viral dynamics model.

Clinical trials for HIV prevention can require knowledge of infection times to subsequently determine protective drug levels. Yet, infection timing is difficult when study visits are sparse. Using population nonlinear mixed-effects (pNLME) statistical inference and viral loads from 46 RV217 study participants, we developed a relatively simple HIV primary infection model that achieved an excellent fit to all data. We also discovered that Aptima assay values from the study strongly correlated with viral loads, enabling imputation of very early viral loads for 28/46 participants. Estimated times between infecting exposures and first positives were generally longer than prior estimates (average of two weeks) and were robust to missing viral upslope data. On simulated data, we found that tighter sampling before diagnosis improved estimation more than tighter sampling after diagnosis. Sampling weekly before and monthly after diagnosis was a pragmatic design for good timing accuracy. Our pNLME timing approach is widely applicable to other infections with existing mathematical models. The present model could be used to simulate future HIV trials and may help estimate protective thresholds from the recently completed antibody-mediated prevention trials.

Factors influencing estimates of HIV-1 infection timing using BEAST.

While large datasets of HIV-1 sequences are increasingly being generated, many studies rely on a single gene or fragment of the genome and few comparative studies across genes have been done. We performed genome-based and gene-specific Bayesian phylogenetic analyses to investigate how certain factors impact estimates of the infection dates in an acute HIV-1 infection cohort, RV217. In this cohort, HIV-1 diagnosis corresponded to the first RNA positive test and occurred a median of four days after the last negative test, allowing us to compare timing estimates using BEAST to a narrow window of infection. We analyzed HIV-1 sequences sampled one week, one month and six months after HIV-1 diagnosis in 39 individuals. We found that shared diversity and temporal signal was limited in acute infection, and insufficient to allow timing inferences in the shortest HIV-1 genes, thus dated phylogenies were primarily analyzed for env, gag, pol and near full-length genomes. There was no one best-fitting model across participants and genes, though relaxed molecular clocks (73% of best-fitting models) and the Bayesian skyline (49%) tended to be favored. For infections with single founders, the infection date was estimated to be around one week pre-diagnosis for env (IQR: 3-9 days) and gag (IQR: 5-9 days), whilst the genome placed it at a median of 10 days (IQR: 4-19). Multiply-founded infections proved problematic to date. Our ability to compare timing inferences to precise estimates of HIV-1 infection (within a week) highlights that molecular dating methods can be applied to within-host datasets from early infection. Nonetheless, our results also suggest caution when using uniform clock and population models or short genes with limited information content.

A SARS-CoV-2 vaccine candidate would likely match all currently circulating variants.

The magnitude of the COVID-19 pandemic underscores the urgency for a safe and effective vaccine. Many vaccine candidates focus on the Spike protein, as it is targeted by neutralizing antibodies and plays a key role in viral entry. Here we investigate the diversity seen in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) sequences and compare it to the sequence on which most vaccine candidates are based. Using 18,514 sequences, we perform phylogenetic, population genetics, and structural bioinformatics analyses. We find limited diversity across SARS-CoV-2 genomes: Only 11 sites show polymorphisms in >5% of sequences; yet two mutations, including the D614G mutation in Spike, have already become consensus. Because SARS-CoV-2 is being transmitted more rapidly than it evolves, the viral population is becoming more homogeneous, with a median of seven nucleotide substitutions between genomes. There is evidence of purifying selection but little evidence of diversifying selection, with substitution rates comparable across structural versus nonstructural genes. Finally, the Wuhan-Hu-1 reference sequence for the Spike protein, which is the basis for different vaccine candidates, matches optimized vaccine inserts, being identical to an ancestral sequence and one mutation away from the consensus. While the rapid spread of the D614G mutation warrants further study, our results indicate that drift and bottleneck events can explain the minimal diversity found among SARS-CoV-2 sequences. These findings suggest that a single vaccine candidate should be efficacious against currently circulating lineages.

Neutralizing antibody VRC01 failed to select for HIV-1 mutations upon viral rebound.

Infusion of the broadly neutralizing antibody VRC01 has been evaluated in individuals chronically infected with HIV-1. Here, we studied how VRC01 infusions affected viral rebound after cessation of antiretroviral therapy (ART) in 18 acutely treated and durably suppressed individuals. Viral rebound occurred in all individuals, yet VRC01 infusions modestly delayed rebound and participants who showed a faster decay of VRC01 in serum rebounded more rapidly. Participants with strains most sensitive to VRC01 or with VRC01 epitope motifs similar to known VRC01-susceptible strains rebounded later. Upon rebound, HIV-1 sequences were indistinguishable from those sampled at diagnosis. Across the cohort, participant-derived Env showed different sensitivity to VRC01 neutralization (including 2 resistant viruses), yet neutralization sensitivity was similar at diagnosis and after rebound, indicating the lack of selection for VRC01 resistance during treatment interruption. Our results showed that viremia rebounded despite the absence of HIV-1 adaptation to VRC01 and an average VRC01 trough of 221 µg/mL. Although VRC01 levels were insufficient to prevent a resurgent infection, knowledge that they did not mediate Env mutations in acute-like viruses is relevant for antibody-based strategies in acute infection.

Molecular dating and viral load growth rates suggested that the eclipse phase lasted about a week in HIV-1 infected adults in East Africa and Thailand.

Most HIV-1 infected individuals do not know their infection dates. Precise infection timing is crucial information for studies that document transmission networks or drug levels at infection. To improve infection timing, we used the prospective RV217 cohort where the window when plasma viremia becomes detectable is narrow: the last negative visit occurred a median of four days before the first detectable HIV-1 viremia with an RNA test, referred below as diagnosis. We sequenced 1,280 HIV-1 genomes from 39 participants at a median of 4, 32 and 170 days post-diagnosis. HIV-1 infections were dated by using sequence-based methods and a viral load regression method. Bayesian coalescent and viral load regression estimated that infections occurred a median of 6 days prior to diagnosis (IQR: 9-3 and 11-4 days prior, respectively). Poisson-Fitter, which analyzes the distribution of hamming distances among sequences, estimated a median of 7 days prior to diagnosis (IQR: 15-4 days) based on sequences sampled 4 days post-diagnosis, but it did not yield plausible results using sequences sampled at 32 days. Fourteen participants reported a high-risk exposure event at a median of 8 days prior to diagnosis (IQR: 12 to 6 days prior). These different methods concurred that HIV-1 infection occurred about a week before detectable viremia, corresponding to 20 days (IQR: 34-15 days) before peak viral load. Together, our methods comparison helps define a framework for future dating studies in early HIV-1 infection.

Biased phylodynamic inferences from analysing clusters of viral sequences.

Phylogenetic methods are being increasingly used to help understand the transmission dynamics of measurably evolving viruses, including HIV. Clusters of highly similar sequences are often observed, which appear to follow a 'power law' behaviour, with a small number of very large clusters. These clusters may help to identify subpopulations in an epidemic, and inform where intervention strategies should be implemented. However, clustering of samples does not necessarily imply the presence of a subpopulation with high transmission rates, as groups of closely related viruses can also occur due to non-epidemiological effects such as over-sampling. It is important to ensure that observed phylogenetic clustering reflects true heterogeneity in the transmitting population, and is not being driven by non-epidemiological effects. We qualify the effect of using a falsely identified 'transmission cluster' of sequences to estimate phylodynamic parameters including the effective population size and exponential growth rate under several demographic scenarios. Our simulation studies show that taking the maximum size cluster to re-estimate parameters from trees simulated under a randomly mixing, constant population size coalescent process systematically underestimates the overall effective population size. In addition, the transmission cluster wrongly resembles an exponential or logistic growth model 99% of the time. We also illustrate the consequences of false clusters in exponentially growing coalescent and birth-death trees, where again, the growth rate is skewed upwards. This has clear implications for identifying clusters in large viral databases, where a false cluster could result in wasted intervention resources.

Genotype-Specific Evolution of Hepatitis E Virus.

Hepatitis E virus (HEV) is the most common cause of acute viral hepatitis globally. HEV comprises four genotypes with different geographic distributions and host ranges. We utilize this natural case-control study for investigating the evolution of zoonotic viruses compared to single-host viruses, using 244 near-full-length HEV genomes. Genome-wide estimates of the ratio of nonsynonymous to synonymous evolutionary changes (dN/dS ratio) located a region of overlapping reading frames, which is subject to positive selection in genotypes 3 and 4. The open reading frames (ORFs) involved have functions related to host-pathogen interaction, so genotype-specific evolution of these regions may reflect their fitness. Bayesian inference of evolutionary rates shows that genotypes 3 and 4 have significantly higher rates than genotype 1 across all ORFs. Reconstruction of the phylogenies of zoonotic genotypes demonstrates significant intermingling of isolates between hosts. We speculate that the genotype-specific differences may result from cyclical adaptation to different hosts in genotypes 3 and 4.IMPORTANCE Hepatitis E virus (HEV) is increasingly recognized as a pathogen that affects both the developing and the developed world. While most often clinically mild, HEV can be severe or fatal in certain demographics, such as expectant mothers. Like many other viral pathogens, HEV has been classified into several distinct genotypes. We show that most of the HEV genome is evolutionarily constrained. One locus of positive selection is unusual in that it encodes two distinct protein products. We are the first to detect positive selection in this overlap region. Genotype 1, which infects humans only, appears to be evolving differently from genotypes 3 and 4, which infect multiple species, possibly because genotypes 3 and 4 are unable to achieve the same fitness due to repeated host jumps.

Phylogenetic Tools for Generalized HIV-1 Epidemics: Findings from the PANGEA-HIV Methods Comparison.

Viral phylogenetic methods contribute to understanding how HIV spreads in populations, and thereby help guide the design of prevention interventions. So far, most analyses have been applied to well-sampled concentrated HIV-1 epidemics in wealthy countries. To direct the use of phylogenetic tools to where the impact of HIV-1 is greatest, the Phylogenetics And Networks for Generalized HIV Epidemics in Africa (PANGEA-HIV) consortium generates full-genome viral sequences from across sub-Saharan Africa. Analyzing these data presents new challenges, since epidemics are principally driven by heterosexual transmission and a smaller fraction of cases is sampled. Here, we show that viral phylogenetic tools can be adapted and used to estimate epidemiological quantities of central importance to HIV-1 prevention in sub-Saharan Africa. We used a community-wide methods comparison exercise on simulated data, where participants were blinded to the true dynamics they were inferring. Two distinct simulations captured generalized HIV-1 epidemics, before and after a large community-level intervention that reduced infection levels. Five research groups participated. Structured coalescent modeling approaches were most successful: phylogenetic estimates of HIV-1 incidence, incidence reductions, and the proportion of transmissions from individuals in their first 3 months of infection correlated with the true values (Pearson correlation > 90%), with small bias. However, on some simulations, true values were markedly outside reported confidence or credibility intervals. The blinded comparison revealed current limits and strengths in using HIV phylogenetics in challenging settings, provided benchmarks for future methods' development, and supports using the latest generation of phylogenetic tools to advance HIV surveillance and prevention.

Rapid host switching in generalist Campylobacter strains erodes the signal for tracing human infections.

Campylobacter jejuni and Campylobacter coli are the biggest causes of bacterial gastroenteritis in the developed world, with human infections typically arising from zoonotic transmission associated with infected meat. Because Campylobacter is not thought to survive well outside the gut, host-associated populations are genetically isolated to varying degrees. Therefore, the likely origin of most strains can be determined by host-associated variation in the genome. This is instructive for characterizing the source of human infection. However, some common strains, notably isolates belonging to the ST-21, ST-45 and ST-828 clonal complexes, appear to have broad host ranges, hindering source attribution. Here whole-genome sequencing has the potential to reveal fine-scale genetic structure associated with host specificity. We found that rates of zoonotic transmission among animal host species in these clonal complexes were so high that the signal of host association is all but obliterated, estimating one zoonotic transmission event every 1.6, 1.8 and 12 years in the ST-21, ST-45 and ST828 complexes, respectively. We attributed 89% of clinical cases to a chicken source, 10% to cattle and 1% to pig. Our results reveal that common strains of C. jejuni and C. coli infectious to humans are adapted to a generalist lifestyle, permitting rapid transmission between different hosts. Furthermore, they show that the weak signal of host association within these complexes presents a challenge for pinpointing the source of clinical infections, underlining the view that whole-genome sequencing, powerful though it is, cannot substitute for intensive sampling of suspected transmission reservoirs.

Measuring Asymmetry in Time-Stamped Phylogenies.

Previous work has shown that asymmetry in viral phylogenies may be indicative of heterogeneity in transmission, for example due to acute HIV infection or the presence of 'core groups' with higher contact rates. Hence, evidence of asymmetry may provide clues to underlying population structure, even when direct information on, for example, stage of infection or contact rates, are missing. However, current tests of phylogenetic asymmetry (a) suffer from false positives when the tips of the phylogeny are sampled at different times and (b) only test for global asymmetry, and hence suffer from false negatives when asymmetry is localised to part of a phylogeny. We present a simple permutation-based approach for testing for asymmetry in a phylogeny, where we compare the observed phylogeny with random phylogenies with the same sampling and coalescence times, to reduce the false positive rate. We also demonstrate how profiles of measures of asymmetry calculated over a range of evolutionary times in the phylogeny can be used to identify local asymmetry. In combination with different metrics of asymmetry, this combined approach offers detailed insights of how phylogenies reconstructed from real viral datasets may deviate from the simplistic assumptions of commonly used coalescent and birth-death process models.

Whole genome sequencing and de novo assembly identifies Sydney-like variant noroviruses and recombinants during the winter 2012/2013 outbreak in England.

BACKGROUND: Norovirus is the commonest cause of epidemic gastroenteritis among people of all ages. Outbreaks frequently occur in hospitals and the community, costing the UK an estimated £110 m per annum. An evolutionary explanation for periodic increases in norovirus cases, despite some host-specific post immunity is currently limited to the identification of obvious recombinants. Our understanding could be significantly enhanced by full length genome sequences for large numbers of intensively sampled viruses, which would also assist control and vaccine design. Our objective is to develop rapid, high-throughput, end-to-end methods yielding complete norovirus genome sequences. We apply these methods to recent English outbreaks, placing them in the wider context of the international norovirus epidemic of winter 2012. METHOD: Norovirus sequences were generated from 28 unique clinical samples by Illumina RNA sequencing (RNA-Seq) of total faecal RNA. A range of de novo sequence assemblers were attempted. The best assembler was identified by validation against three replicate samples and two norovirus qPCR negative samples, together with an additional 20 sequences determined by PCR and fractional capillary sequencing. Phylogenetic methods were used to reconstruct evolutionary relationships from the whole genome sequences. RESULTS: Full length norovirus genomes were generated from 23/28 samples. 5/28 partial norovirus genomes were associated with low viral copy numbers. The de novo assembled sequences differed from sequences determined by capillary sequencing by <0.003%. Intra-host nucleotide sequence diversity was rare, but detectable by mapping short sequence reads onto its de novo assembled consensus. Genomes similar to the Sydney 2012 strain caused 78% (18/23) of cases, consistent with its previously documented association with the winter 2012 global outbreak. Interestingly, phylogenetic analysis and recombination detection analysis of the consensus sequences identified two related viruses as recombinants, containing sequences in prior circulation to Sydney 2012 in open reading frame (ORF) 2. CONCLUSION: Our approach facilitates the rapid determination of complete norovirus genomes. This method provides high resolution of full norovirus genomes which, when coupled with detailed epidemiology, may improve the understanding of evolution and control of this important healthcare-associated pathogen.

Coalescent inference for infectious disease: meta-analysis of hepatitis C.

Genetic analysis of pathogen genomes is a powerful approach to investigating the population dynamics and epidemic history of infectious diseases. However, the theoretical underpinnings of the most widely used, coalescent methods have been questioned, casting doubt on their interpretation. The aim of this study is to develop robust population genetic inference for compartmental models in epidemiology. Using a general approach based on the theory of metapopulations, we derive coalescent models under susceptible-infectious (SI), susceptible-infectious-susceptible (SIS) and susceptible-infectious-recovered (SIR) dynamics. We show that exponential and logistic growth models are equivalent to SI and SIS models, respectively, when co-infection is negligible. Implementing SI, SIS and SIR models in BEAST, we conduct a meta-analysis of hepatitis C epidemics, and show that we can directly estimate the basic reproductive number (R(0)) and prevalence under SIR dynamics. We find that differences in genetic diversity between epidemics can be explained by differences in underlying epidemiology (age of the epidemic and local population density) and viral subtype. Model comparison reveals SIR dynamics in three globally restricted epidemics, but most are better fit by the simpler SI dynamics. In summary, metapopulation models provide a general and practical framework for integrating epidemiology and population genetics for the purposes of joint inference.