Genetic drift and purifying selection shape within-host influenza A virus populations during natural swine infections.

Patterns of within-host influenza A virus (IAV) diversity and evolution have been described in natural human infections, but these patterns remain poorly characterized in non-human hosts. Elucidating these dynamics is important to better understand IAV biology and the evolutionary processes that govern spillover into humans. Here, we sampled an IAV outbreak in pigs during a week-long county fair to characterize viral diversity and evolution in this important reservoir host. Nasal wipes were collected on a daily basis from all pigs present at the fair, yielding up to 421 samples per day. Subtyping of PCR-positive samples revealed the co-circulation of H1N1 and H3N2 subtype swine IAVs. PCR-positive samples with robust Ct values were deep-sequenced, yielding 506 sequenced samples from a total of 253 pigs. Based on higher-depth re-sequenced data from a subset of these initially sequenced samples (260 samples from 168 pigs), we characterized patterns of within-host IAV genetic diversity and evolution. We find that IAV genetic diversity in single-subtype infected pigs is low, with the majority of intrahost Single Nucleotide Variants (iSNVs) present at frequencies of <10%. The ratio of the number of nonsynonymous to the number of synonymous iSNVs is significantly lower than under the neutral expectation, indicating that purifying selection shapes patterns of within-host viral diversity in swine. The dynamic turnover of iSNVs and their pronounced frequency changes further indicate that genetic drift also plays an important role in shaping IAV populations within pigs. Taken together, our results highlight similarities in patterns of IAV genetic diversity and evolution between humans and swine, including the role of stochastic processes in shaping within-host IAV dynamics.

Genetic drift and purifying selection shape within-host influenza A virus populations during natural swine infections.

Patterns of within-host influenza A virus (IAV) diversity and evolution have been described in natural human infections, but these patterns remain poorly characterized in non-human hosts. Elucidating these dynamics is important to better understand IAV biology and the evolutionary processes that govern spillover into humans. Here, we sampled an IAV outbreak in pigs during a week-long county fair to characterize viral diversity and evolution in this important reservoir host. Nasal wipes were collected on a daily basis from all pigs present at the fair, yielding up to 421 samples per day. Subtyping of PCR-positive samples revealed the co-circulation of H1N1 and H3N2 subtype IAVs. PCR-positive samples with robust Ct values were deep-sequenced, yielding 506 sequenced samples from a total of 253 pigs. Based on higher-depth re-sequenced data from a subset of these initially sequenced samples (260 samples from 168 pigs), we characterized patterns of within-host IAV genetic diversity and evolution. We find that IAV genetic diversity in single-subtype infected pigs is low, with the majority of intra-host single nucleotide variants (iSNVs) present at frequencies of <10%. The ratio of the number of nonsynonymous to the number of synonymous iSNVs is significantly lower than under the neutral expectation, indicating that purifying selection shapes patterns of within-host viral diversity in swine. The dynamic turnover of iSNVs and their pronounced frequency changes further indicate that genetic drift also plays an important role in shaping IAV populations within pigs. Taken together, our results highlight similarities in patterns of IAV genetic diversity and evolution between humans and swine, including the role of stochastic processes in shaping within-host IAV dynamics.

Genomic Surveillance for SARS-CoV-2 Variants: Circulation of Omicron Lineages - United States, January 2022-May 2023.

CDC has used national genomic surveillance since December 2020 to monitor SARS-CoV-2 variants that have emerged throughout the COVID-19 pandemic, including the Omicron variant. This report summarizes U.S. trends in variant proportions from national genomic surveillance during January 2022-May 2023. During this period, the Omicron variant remained predominant, with various descendant lineages reaching national predominance (>50% prevalence). During the first half of 2022, BA.1.1 reached predominance by the week ending January 8, 2022, followed by BA.2 (March 26), BA.2.12.1 (May 14), and BA.5 (July 2); the predominance of each variant coincided with surges in COVID-19 cases. The latter half of 2022 was characterized by the circulation of sublineages of BA.2, BA.4, and BA.5 (e.g., BQ.1 and BQ.1.1), some of which independently acquired similar spike protein substitutions associated with immune evasion. By the end of January 2023, XBB.1.5 became predominant. As of May 13, 2023, the most common circulating lineages were XBB.1.5 (61.5%), XBB.1.9.1 (10.0%), and XBB.1.16 (9.4%); XBB.1.16 and XBB.1.16.1 (2.4%), containing the K478R substitution, and XBB.2.3 (3.2%), containing the P521S substitution, had the fastest doubling times at that point. Analytic methods for estimating variant proportions have been updated as the availability of sequencing specimens has declined. The continued evolution of Omicron lineages highlights the importance of genomic surveillance to monitor emerging variants and help guide vaccine development and use of therapeutics.

SARS-CoV-2 Delta-Omicron Recombinant Viruses, United States.

To detect new and changing SARS-CoV-2 variants, we investigated candidate Delta-Omicron recombinant genomes from Centers for Disease Control and Prevention national genomic surveillance. Laboratory and bioinformatic investigations identified and validated 9 genetically related SARS-CoV-2 viruses with a hybrid Delta-Omicron spike protein.

Genomic Surveillance for SARS-CoV-2 Variants: Predominance of the Delta (B.1.617.2) and Omicron (B.1.1.529) Variants - United States, June 2021-January 2022.

Genomic surveillance is a critical tool for tracking emerging variants of SARS-CoV-2 (the virus that causes COVID-19), which can exhibit characteristics that potentially affect public health and clinical interventions, including increased transmissibility, illness severity, and capacity for immune escape. During June 2021-January 2022, CDC expanded genomic surveillance data sources to incorporate sequence data from public repositories to produce weighted estimates of variant proportions at the jurisdiction level and refined analytic methods to enhance the timeliness and accuracy of national and regional variant proportion estimates. These changes also allowed for more comprehensive variant proportion estimation at the jurisdictional level (i.e., U.S. state, district, territory, and freely associated state). The data in this report are a summary of findings of recent proportions of circulating variants that are updated weekly on CDC's COVID Data Tracker website to enable timely public health action.† The SARS-CoV-2 Delta (B.1.617.2 and AY sublineages) variant rose from 1% to >50% of viral lineages circulating nationally during 8 weeks, from May 1-June 26, 2021. Delta-associated infections remained predominant until being rapidly overtaken by infections associated with the Omicron (B.1.1.529 and BA sublineages) variant in December 2021, when Omicron increased from 1% to >50% of circulating viral lineages during a 2-week period. As of the week ending January 22, 2022, Omicron was estimated to account for 99.2% (95% CI = 99.0%-99.5%) of SARS-CoV-2 infections nationwide, and Delta for 0.7% (95% CI = 0.5%-1.0%). The dynamic landscape of SARS-CoV-2 variants in 2021, including Delta- and Omicron-driven resurgences of SARS-CoV-2 transmission across the United States, underscores the importance of robust genomic surveillance efforts to inform public health planning and practice.

Novel and extendable genotyping system for human respiratory syncytial virus based on whole-genome sequence analysis.

BACKGROUND: Human respiratory syncytial virus (RSV) is one of the leading causes of respiratory infections, especially in infants and young children. Previous RSV sequencing studies have primarily focused on partial sequencing of G gene (200-300 nucleotides) for genotype characterization or diagnostics. However, the genotype assignment with G gene has not recapitulated the phylogenetic signal of other genes, and there is no consensus on RSV genotype definition. METHODS: We conducted maximum likelihood phylogenetic analysis with 10 RSV individual genes and whole-genome sequence (WGS) that are published in GenBank. RSV genotypes were determined by using phylogenetic analysis and pair-wise node distances. RESULTS: In this study, we first statistically examined the phylogenetic incongruence, rate variation for each RSV gene sequence and WGS. We then proposed a new RSV genotyping system based on a comparative analysis of WGS and the temporal distribution of strains. We also provide an RSV classification tool to perform RSV genotype assignment and a publicly accessible up-to-date instance of Nextstrain where the phylogenetic relationship of all genotypes can be explored. CONCLUSIONS: This revised RSV genotyping system will provide important information for disease surveillance, epidemiology, and vaccine development.

Avian Influenza A Viruses Reassort and Diversify Differently in Mallards and Mammals.

Reassortment among co-infecting influenza A viruses (IAVs) is an important source of viral diversity and can facilitate expansion into novel host species. Indeed, reassortment played a key role in the evolution of the last three pandemic IAVs. Observed patterns of reassortment within a coinfected host are likely to be shaped by several factors, including viral load, the extent of viral mixing within the host and the stringency of selection. These factors in turn are expected to vary among the diverse host species that IAV infects. To investigate host differences in IAV reassortment, here we examined reassortment of two distinct avian IAVs within their natural host (mallards) and a mammalian model system (guinea pigs). Animals were co-inoculated with A/wildbird/California/187718-36/2008 (H3N8) and A/mallard/Colorado/P66F1-5/2008 (H4N6) viruses. Longitudinal samples were collected from the cloaca of mallards or the nasal tract of guinea pigs and viral genetic exchange was monitored by genotyping clonal isolates from these samples. Relative to those in guinea pigs, viral populations in mallards showed higher frequencies of reassortant genotypes and were characterized by higher genotype richness and diversity. In line with these observations, analysis of pairwise segment combinations revealed lower linkage disequilibrium in mallards as compared to guinea pigs. No clear longitudinal patterns in richness, diversity or linkage disequilibrium were present in either host. Our results reveal mallards to be a highly permissive host for IAV reassortment and suggest that reduced viral mixing limits avian IAV reassortment in a mammalian host.

Impact of Immune Priming, Vaccination, and Infection on Influenza A(H3N2) Antibody Landscapes in Children.

BACKGROUND: Preexisting antibodies to influenza, shaped by early infection and subsequent exposures, may impact responses to influenza vaccination. METHODS: We enrolled 72 children (aged 7-17 years) in 2015-2016; all received inactivated influenza vaccines. Forty-one were also vaccinated in 2014-2015, with 12 becoming infected with A(H3N2) in 2014-2015. Thirty-one children did not have documented influenza exposures in the prior 5 seasons. Sera were collected pre- and postvaccination in both seasons. We constructed antibody landscapes using hemagglutination inhibition antibody titers against 16 A(H3N2) viruses representative of major antigenic clusters that circulated between 1968 and 2015. RESULTS: The breadth of the antibody landscapes increased with age. Vaccine-induced antibody responses correlated with boosting of titers to previously encountered antigens. Postvaccination titers were the highest against vaccine antigens rather than the historic A(H3N2) viruses previously encountered. Prevaccination titers to the vaccine were the strongest predictors of postvaccination titers. Responses to vaccine antigens did not differ by likely priming virus. Influenza A(H3N2)-infected children in 2014-2015 had narrower antibody landscapes than those uninfected, but prior season infection status had little effect on antibody landscapes following 2015-2016 vaccination. CONCLUSIONS: A(H3N2) antibody landscapes in children were largely determined by age-related immune priming, rather than recent vaccination or infection.

Author Correction: Direct RNA Sequencing of the Coding Complete Influenza A Virus Genome.

A correction to this article has been published and is linked from the HTML and PDF versions of this paper. The error has been fixed in the paper.

Direct RNA Sequencing of the Coding Complete Influenza A Virus Genome.

For the first time, a coding complete genome of an RNA virus has been sequenced in its original form. Previously, RNA was sequenced by the chemical degradation of radiolabeled RNA, a difficult method that produced only short sequences. Instead, RNA has usually been sequenced indirectly by copying it into cDNA, which is often amplified to dsDNA by PCR and subsequently analyzed using a variety of DNA sequencing methods. We designed an adapter to short highly conserved termini of the influenza A virus genome to target the (-) sense RNA into a protein nanopore on the Oxford Nanopore MinION sequencing platform. Utilizing this method with total RNA extracted from the allantoic fluid of influenza rA/Puerto Rico/8/1934 (H1N1) virus infected chicken eggs (EID50 6.8 × 109), we demonstrate successful sequencing of the coding complete influenza A virus genome with 100% nucleotide coverage, 99% consensus identity, and 99% of reads mapped to influenza A virus. By utilizing the same methodology one can redesign the adapter in order to expand the targets to include viral mRNA and (+) sense cRNA, which are essential to the viral life cycle, or other pathogens. This approach also has the potential to identify and quantify splice variants and base modifications, which are not practically measurable with current methods.

Antigenic characterization of highly pathogenic avian influenza A(H5N1) viruses with chicken and ferret antisera reveals clade-dependent variation in hemagglutination inhibition profiles.

Highly pathogenic avian influenza (HPAI) A(H5N1) viruses pose a significant economic burden to the poultry industry worldwide and have pandemic potential. Poultry vaccination against HPAI A(H5N1) viruses has been an important component of HPAI control measures and has been performed in Vietnam since 2005. To systematically assess antigenic matching of current vaccines to circulating field variants, we produced a panel of chicken and ferret antisera raised against historical and contemporary Vietnamese reference viruses representing clade variants that were detected between 2001 and 2014. The antisera were used for hemagglutination inhibition (HI) assays to generate data sets for analysis by antigenic cartography, allowing for a direct comparison of results from chicken or ferret antisera. HI antigenic maps, developed with antisera from both hosts, revealed varying patterns of antigenic relationships and clustering of viruses that were dependent on the clade of viruses analyzed. Antigenic relationships between existing poultry vaccines and circulating field viruses were also aligned with in vivo protection profiles determined by previously reported vaccine challenge studies. Our results establish the feasibility and utility of HPAI A(H5N1) antigenic characterization using chicken antisera and support further experimental and modeling studies to investigate quantitative relationships between genetic variation, antigenic drift and correlates of poultry vaccine protection in vivo.

Active Ebola Virus Replication and Heterogeneous Evolutionary Rates in EVD Survivors.

Following cessation of continuous Ebola virus (EBOV) transmission within Western Africa, sporadic EBOV disease (EVD) cases continued to re-emerge beyond the viral incubation period. Epidemiological and genomic evidence strongly suggests that this represented transmission from EVD survivors. To investigate whether persistent infections are characterized by ongoing viral replication, we sequenced EBOV from the semen of nine EVD survivors and a subset of corresponding acute specimens. EBOV evolutionary rates during persistence were either similar to or reduced relative to acute infection rates. Active EBOV replication/transcription continued during convalescence, but decreased over time, consistent with viral persistence rather than viral latency. Patterns of genetic divergence suggest a moderate relaxation of selective constraints within the sGP carboxy-terminal tail during persistent infections, but do not support widespread diversifying selection. Altogether, our data illustrate that EBOV persistence in semen, urine, and aqueous humor is not a quiescent or latent infection.

Shifting Clade Distribution, Reassortment, and Emergence of New Subtypes of Highly Pathogenic Avian Influenza A(H5) Viruses Collected from Vietnamese Poultry from 2012 to 2015.

Whole-genome sequences of representative highly pathogenic avian influenza A(H5) viruses from Vietnam were generated, comprising samples from poultry outbreaks and active market surveillance collected from January 2012 to August 2015. Six hemagglutinin gene clades were characterized. Clade 1.1.2 was predominant in southern Mekong provinces throughout 2012 and 2013 but gradually disappeared and was not detected after April 2014. Clade 2.3.2.1c viruses spread rapidly during 2012 and were detected in the south and center of the country. A number of clade 1.1.2 and 2.3.2.1c interclade reassortant viruses were detected with different combinations of internal genes derived from 2.3.2.1a and 2.3.2.1b viruses, indicating extensive cocirculation. Although reassortment generated genetic diversity at the genotype level, there was relatively little genetic drift within the individual gene segments, suggesting genetic stasis over recent years. Antigenically, clade 1.1.2, 2.3.2.1a, 2.3.2.1b, and 2.3.2.1c viruses remained related to earlier viruses and WHO-recommended prepandemic vaccine strains representing these clades. Clade 7.2 viruses, although detected in only low numbers, were the exception, as indicated by introduction of a genetically and antigenically diverse strain in 2013. Clade 2.3.4.4 viruses (H5N1 and H5N6) were likely introduced in April 2014 and appeared to gain dominance across northern and central regions. Antigenic analyses of clade 2.3.4.4 viruses compared to existing clade 2.3.4 candidate vaccine viruses (CVV) indicated the need for an updated vaccine virus. A/Sichuan/26221/2014 (H5N6) virus was developed, and ferret antisera generated against this virus were demonstrated to inhibit some but not all clade 2.3.4.4 viruses, suggesting consideration of alternative clade 2.3.4.4 CVVs.IMPORTANCE Highly pathogenic avian influenza (HPAI) A(H5) viruses have circulated continuously in Vietnam since 2003, resulting in hundreds of poultry outbreaks and sporadic human infections. Despite a significant reduction in the number of human infections in recent years, poultry outbreaks continue to occur and the virus continues to diversify. Vaccination of poultry has been used as a means to control the spread and impact of the virus, but due to the diversity and changing distribution of antigenically distinct viruses, the utility of vaccines in the face of mismatched circulating strains remains questionable. This study assessed the putative amino acid changes in viruses leading to antigenic variability, underscoring the complexity of vaccine selection for both veterinary and public health purposes. Given the overlapping geographic distributions of multiple, antigenically distinct clades of HPAI A(H5) viruses in Vietnam, the vaccine efficacy of bivalent poultry vaccine formulations should be tested in the future.

Viral deep sequencing needs an adaptive approach: IRMA, the iterative refinement meta-assembler.

BACKGROUND: Deep sequencing makes it possible to observe low-frequency viral variants and sub-populations with greater accuracy and sensitivity than ever before. Existing platforms can be used to multiplex a large number of samples; however, analysis of the resulting data is complex and involves separating barcoded samples and various read manipulation processes ending in final assembly. Many assembly tools were designed with larger genomes and higher fidelity polymerases in mind and do not perform well with reads derived from highly variable viral genomes. Reference-based assemblers may leave gaps in viral assemblies while de novo assemblers may struggle to assemble unique genomes. RESULTS: The IRMA (iterative refinement meta-assembler) pipeline solves the problem of viral variation by the iterative optimization of read gathering and assembly. As with all reference-based assembly, reads are included in assembly when they match consensus template sets; however, IRMA provides for on-the-fly reference editing, correction, and optional elongation without the need for additional reference selection. This increases both read depth and breadth. IRMA also focuses on quality control, error correction, indel reporting, variant calling and variant phasing. In fact, IRMA's ability to detect and phase minor variants is one of its most distinguishing features. We have built modules for influenza and ebolavirus. We demonstrate usage and provide calibration data from mixture experiments. Methods for variant calling, phasing, and error estimation/correction have been redesigned to meet the needs of viral genomic sequencing. CONCLUSION: IRMA provides a robust next-generation sequencing assembly solution that is adapted to the needs and characteristics of viral genomes. The software solves issues related to the genetic diversity of viruses while providing customized variant calling, phasing, and quality control. IRMA is freely available for non-commercial use on Linux and Mac OS X and has been parallelized for high-throughput computing.

Preliminary Evaluation of the Effect of Investigational Ebola Virus Disease Treatments on Viral Genome Sequences.

BACKGROUND: Several patients with Ebola virus disease (EVD) managed in the United States have received ZMapp monoclonal antibodies, TKM-Ebola small interfering RNA, brincidofovir, and/or convalescent plasma as investigational therapeutics. METHODS: To investigate whether treatment selected for Ebola virus (EBOV) mutations conferring resistance, viral sequencing was performed on RNA extracted from clinical blood specimens from patients with EVD following treatment, and putative viral targets were analyzed. RESULTS: We observed no major or minor EBOV mutations within regions targeted by therapeutics. CONCLUSIONS: This small subset of patients and clinical specimens suggests that evolution of resistance is not a direct consequence of antiviral treatment. As EVD antiviral treatments are introduced into wider use, it is essential that continuous viral full-genome surveillance is performed, to monitor for the emergence of escape mutations.

Improving pandemic influenza risk assessment.

Assessing the pandemic risk posed by specific non-human influenza A viruses is an important goal in public health research. As influenza virus genome sequencing becomes cheaper, faster, and more readily available, the ability to predict pandemic potential from sequence data could transform pandemic influenza risk assessment capabilities. However, the complexities of the relationships between virus genotype and phenotype make such predictions extremely difficult. The integration of experimental work, computational tool development, and analysis of evolutionary pathways, together with refinements to influenza surveillance, has the potential to transform our ability to assess the risks posed to humans by non-human influenza viruses and lead to improved pandemic preparedness and response.

LABEL: fast and accurate lineage assignment with assessment of H5N1 and H9N2 influenza A hemagglutinins.

The evolutionary classification of influenza genes into lineages is a first step in understanding their molecular epidemiology and can inform the subsequent implementation of control measures. We introduce a novel approach called Lineage Assignment By Extended Learning (LABEL) to rapidly determine cladistic information for any number of genes without the need for time-consuming sequence alignment, phylogenetic tree construction, or manual annotation. Instead, LABEL relies on hidden Markov model profiles and support vector machine training to hierarchically classify gene sequences by their similarity to pre-defined lineages. We assessed LABEL by analyzing the annotated hemagglutinin genes of highly pathogenic (H5N1) and low pathogenicity (H9N2) avian influenza A viruses. Using the WHO/FAO/OIE H5N1 evolution working group nomenclature, the LABEL pipeline quickly and accurately identified the H5 lineages of uncharacterized sequences. Moreover, we developed an updated clade nomenclature for the H9 hemagglutinin gene and show a similarly fast and reliable phylogenetic assessment with LABEL. While this study was focused on hemagglutinin sequences, LABEL could be applied to the analysis of any gene and shows great potential to guide molecular epidemiology activities, accelerate database annotation, and provide a data sorting tool for other large-scale bioinformatic studies.

Structural and antigenic variation among diverse clade 2 H5N1 viruses.

Antigenic variation among circulating H5N1 highly pathogenic avian influenza A viruses mandates the continuous production of strain-specific pre-pandemic vaccine candidates and represents a significant challenge for pandemic preparedness. Here we assessed the structural, antigenic and receptor-binding properties of three H5N1 HPAI virus hemagglutinins, which were recently selected by the WHO as vaccine candidates [A/Egypt/N03072/2010 (Egypt10, clade 2.2.1), A/Hubei/1/2010 (Hubei10, clade 2.3.2.1) and A/Anhui/1/2005 (Anhui05, clade 2.3.4)]. These analyses revealed that antigenic diversity among these three isolates was restricted to changes in the size and charge of amino acid side chains at a handful of positions, spatially equivalent to the antigenic sites identified in H1 subtype viruses circulating among humans. All three of the H5N1 viruses analyzed in this study were responsible for fatal human infections, with the most recently-isolated strains, Hubei10 and Egypt10, containing multiple residues in the receptor-binding site of the HA, which were suspected to enhance mammalian transmission. However, glycan-binding analyses demonstrated a lack of binding to human α2-6-linked sialic acid receptor analogs for all three HAs, reinforcing the notion that receptor-binding specificity contributes only partially to transmissibility and pathogenesis of HPAI viruses and suggesting that changes in host specificity must be interpreted in the context of the host and environmental factors, as well as the virus as a whole. Together, our data reveal structural linkages with phylogenetic and antigenic analyses of recently emerged H5N1 virus clades and should assist in interpreting the significance of future changes in antigenic and receptor-binding properties.

Exploiting mid-range DNA patterns for sequence classification: binary abstraction Markov models.

Messenger RNA sequences possess specific nucleotide patterns distinguishing them from non-coding genomic sequences. In this study, we explore the utilization of modified Markov models to analyze sequences up to 44 bp, far beyond the 8-bp limit of conventional Markov models, for exon/intron discrimination. In order to analyze nucleotide sequences of this length, their information content is first reduced by conversion into shorter binary patterns via the application of numerous abstraction schemes. After the conversion of genomic sequences to binary strings, homogenous Markov models trained on the binary sequences are used to discriminate between exons and introns. We term this approach the Binary Abstraction Markov Model (BAMM). High-quality abstraction schemes for exon/intron discrimination are selected using optimization algorithms on supercomputers. The best MM classifiers are then combined using support vector machines into a single classifier. With this approach, over 95% classification accuracy is achieved without taking reading frame into account. With further development, the BAMM approach can be applied to sequences lacking the genetic code such as ncRNAs and 5'-untranslated regions.