Effect of human H3N2 influenza virus reassortment on influenza incidence and severity during the 2017-18 influenza season in the USA: a retrospective observational genomic analysis.

BACKGROUND: During the 2017-18 influenza season in the USA, there was a high incidence of influenza illness and mortality. However, no apparent antigenic change was identified in the dominant H3N2 viruses, and the severity of the season could not be solely attributed to a vaccine mismatch. We aimed to investigate whether the altered virus properties resulting from gene reassortment were underlying causes of the increased case number and disease severity associated with the 2017-18 influenza season. METHODS: Samples included were collected from patients with influenza who were prospectively recruited during the 2016-17 and 2017-18 influenza seasons at the Johns Hopkins Hospital Emergency Departments in Baltimore, MD, USA, as well as from archived samples from Johns Hopkins Health System sites. Among 647 recruited patients with influenza A virus infection, 411 patients with whole-genome sequences were available in the Johns Hopkins Center of Excellence for Influenza Research and Surveillance network during the 2016-17 and 2017-18 seasons. Phylogenetic trees were constructed based on viral whole-genome sequences. Representative viral isolates of the two seasons were characterised in immortalised cell lines and human nasal epithelial cell cultures, and patients' demographic data and clinical outcomes were analysed. FINDINGS: Unique H3N2 reassortment events were observed, resulting in two predominant strains in the 2017-18 season: HA clade 3C.2a2 and clade 3C.3a, which had novel gene segment constellations containing gene segments from HA clade 3C.2a1 viruses. The reassortant re3C.2a2 viruses replicated with faster kinetics and to a higher peak titre compared with the parental 3C.2a2 and 3C.2a1 viruses (48 h vs 72 h). Furthermore, patients infected with reassortant 3C.2a2 viruses had higher Influenza Severity Scores than patients infected with the parental 3C.2a2 viruses (median 3·00 [IQR 1·00-4·00] vs 1·50 [1·00-2·00]; p=0·018). INTERPRETATION: Our findings suggest that the increased severity of the 2017-18 influenza season was due in part to two intrasubtypes, cocirculating H3N2 reassortant viruses with fitness advantages over the parental viruses. This information could help inform future vaccine development and public health policies. FUNDING: The Center of Excellence for Influenza Research and Response in the US, National Science and Technology Council, and Chang Gung Memorial Hospital in Taiwan.

MutaGAN: A sequence-to-sequence GAN framework to predict mutations of evolving protein populations.

The ability to predict the evolution of a pathogen would significantly improve the ability to control, prevent, and treat disease. Machine learning, however, is yet to be used to predict the evolutionary progeny of a virus. To address this gap, we developed a novel machine learning framework, named MutaGAN, using generative adversarial networks with sequence-to-sequence, recurrent neural networks generator to accurately predict genetic mutations and evolution of future biological populations. MutaGAN was trained using a generalized time-reversible phylogenetic model of protein evolution with maximum likelihood tree estimation. MutaGAN was applied to influenza virus sequences because influenza evolves quickly and there is a large amount of publicly available data from the National Center for Biotechnology Information's Influenza Virus Resource. MutaGAN generated 'child' sequences from a given 'parent' protein sequence with a median Levenshtein distance of 4.00 amino acids. Additionally, the generator was able to generate sequences that contained at least one known mutation identified within the global influenza virus population for 72.8 per cent of parent sequences. These results demonstrate the power of the MutaGAN framework to aid in pathogen forecasting with implications for broad utility in evolutionary prediction for any protein population.

Enhancing global health security in Thailand: Strengths and challenges of initiating a One Health approach to avian influenza surveillance.

Infectious disease surveillance systems support early warning, promote preparedness, and inform public health response. Pathogens that have human, animal, and environmental reservoirs should be monitored through systems that incorporate a One Health approach. In 2016, Thailand's federal government piloted an avian influenza (AI) surveillance system that integrates stakeholders from human, animal, and environmental sectors, at the central level and in four provinces to monitor influenza A viruses within human, waterfowl, and poultry populations. This research aims to describe and evaluate Thailand's piloted AI surveillance system to inform strategies for strengthening and building surveillance systems relevant to One Health. We assessed this surveillance system using the United States Centers for Disease Control and Prevention's (U.S. CDC) "Guidelines for Evaluating Public Health Surveillance Systems" and added three novel metrics: transparency, interoperability, and security. In-depth key informant interviews were conducted with representatives among six Thai federal agencies and departments, the One Health coordinating unit, a corporate poultry producer, and the Thai Ministry of Public Health-U.S. CDC Collaborating Unit. Thailand's AI surveillance system demonstrated strengths in acceptability, simplicity, representativeness, and flexibility, and exhibited challenges in data quality, stability, security, interoperability, and transparency. System efforts may be strengthened through increasing laboratory integration, improving pathogen detection capabilities, implementing interoperable systems, and incorporating sustainable capacity building mechanisms. This innovative piloted surveillance system provides a strategic framework that can be used to develop, integrate, and bolster One Health surveillance approaches to combat emerging global pathogen threats and enhance global health security.

Evolutionary history and introduction of SARS-CoV-2 Alpha VOC/B.1.1.7 in Pakistan through international travelers.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants continue to emerge, and their identification is important for the public health response to coronavirus disease 2019 (COVID-19). Genomic sequencing provides robust information but may not always be accessible, and therefore, mutation-based polymerase chain reaction (PCR) approaches can be used for rapid identification of known variants. International travelers arriving in Karachi between December 2020 and February 2021 were tested for SARS-CoV-2 by PCR. A subset of positive samples was tested for S-gene target failure (SGTF) on TaqPathTM COVID-19 (Thermo Fisher Scientific) and for mutations using the GSD NovaType SARS-CoV-2 (Eurofins Technologies) assays. Sequencing was conducted on the MinION platform (Oxford Nanopore Technologies). Bayesian phylogeographic inference was performed integrating the patients' travel history information. Of the thirty-five COVID-19 cases screened, thirteen had isolates with SGTF. The travelers transmitted infection to sixty-eight contact cases. The B.1.1.7 lineage was confirmed through sequencing and PCR. The phylogenetic analysis of sequence data available for six cases included four B.1.1.7 strains and one B.1.36 and B.1.1.212 lineage isolate. Phylogeographic modeling estimated at least three independent B.1.1.7 introductions into Karachi, Pakistan, originating from the UK. B.1.1.212 and B.1.36 were inferred to be introduced either from the UK or the travelers' layover location. We report the introduction of SARS-CoV-2 B.1.1.7 and other lineages in Pakistan by international travelers arriving via different flight routes. This highlights SARS-CoV-2 transmission through travel, importance of testing, and quarantine post-travel to prevent transmission of new strains, as well as recording detailed patients' metadata. Such results help inform policies on restricting travel from destinations where new highly transmissible variants have emerged.

Nosocomial Respiratory Infections in a Rural Zambian Hospital.

The burden of nosocomial respiratory infections in rural southern Africa is poorly understood. We established a surveillance program at a rural Zambian hospital to detect influenza-like illness (ILI) and respiratory infections among hospitalized patients and a cohort of healthcare workers (HCWs). Nasopharyngeal specimens from symptomatic patients and HCWs underwent broadly multiplexed molecular testing to detect viruses and atypical bacteria. During 1 year of surveillance, 15 patients (1.7% of admissions) developed ILI more than 48 hours after admission. Among 44 HCWs, 19 (43%) experienced at least one ILI episode, with a total of 31 ILI episodes detected. Respiratory viruses were detected in 45% of patient and 55% of HCW specimens. The cumulative incidence of influenza infection among HCWs over 1 year was 9%. Overall, respiratory viruses were commonly found among patients and HCWs in a rural Zambian hospital with limited infection control infrastructure.

Identification of H3N2 NA and PB1-F2 genetic variants and their association with disease symptoms during the 2014-15 influenza season.

The 2014-15 influenza season saw the emergence of an H3N2 antigenic drift variant that formed the 3C.2a HA clade. Whole viral genomes were sequenced from nasopharyngeal swabs of ninety-four patients with confirmed influenza A virus infection and primary human nasal epithelial cell cultures used to efficiently isolate H3N2 viruses. The isolates were classified by HA clade and the presence of a new set of co-selected mutations in NA (a glycosylation site, NAg+) and PB1-F2 (H75P). The NA and PB1-F2 mutations were present in a subset of clade 3C.2a viruses (NAg+F2P), which dominated during the subsequent influenza seasons. In human nasal epithelial cell cultures, a virus with the novel NAg+F2P genotype replicated less well compared with a virus with the parental genotype. Retrospective analyses of clinical data showed that NAg+F2P genotype viruses were associated with increased cough and shortness of breath in infected patients.

Differential disease severity and whole-genome sequence analysis for human influenza A/H1N1pdm virus in 2015-2016 influenza season.

During the 2015-16 winter, the US experienced a relatively mild influenza season compared to Taiwan, which had a higher number of total and severe cases. While H1N1pdm viruses dominated global surveillance efforts that season, the global distribution of genetic variants and their contributions to disease severity have not been investigated. Samples collected from influenza A-positive patients by the Johns Hopkins Center of Excellence for Influenza Research and Surveillance active surveillance in the emergency rooms in Baltimore, Maryland, USA, and northern Taiwan between November 2015 and April 2016, were processed for influenza A virus whole-genome sequencing. In Baltimore, the majority of the viruses were the H1N1pdm clade 6B.1 and no H1N1pdm clade 6B.2 viruses were detected. In northern Taiwan, more than half of the H1N1pdm viruses were clade 6B.1 and 38% were clade 6B.2, consistent with the global observation that most 6B.2 viruses circulated in Asia and not North America. Whole virus genome sequence analysis identified two genetic subgroups present in each of the 6B.1 and 6B.2 clades and one 6B.1 interclade reassortant virus. Clinical data showed 6B.2 patients had more disease symptoms including higher crude and inverse probability weighted odds of pneumonia than 6B.1 patients, suggesting 6B.2 circulation may be one of the reasons for the severe flu season in Taiwan. Local surveillance efforts linking H1N1pdm virus sequences to patient clinical and demographic data improve our understanding of influenza circulation and disease potential.

Genomic diversity of SARS-CoV-2 during early introduction into the Baltimore-Washington metropolitan area.

The early COVID-19 pandemic was characterized by rapid global spread. In Maryland and Washington, DC, United States, more than 2500 cases were reported within 3 weeks of the first COVID-19 detection in March 2020. We aimed to use genomic sequencing to understand the initial spread of SARS-CoV-2 - the virus that causes COVID-19 - in the region. We analyzed 620 samples collected from the Johns Hopkins Health System during March 11-31, 2020, comprising 28.6% of the total cases in Maryland and Washington, DC. From these samples, we generated 114 complete viral genomes. Analysis of these genomes alongside a subsampling of over 1000 previously published sequences showed that the diversity in this region rivaled global SARS-CoV-2 genetic diversity at that time and that the sequences belong to all of the major globally circulating lineages, suggesting multiple introductions into the region. We also analyzed these regional SARS-CoV-2 genomes alongside detailed clinical metadata and found that clinically severe cases had viral genomes belonging to all major viral lineages. We conclude that efforts to control local spread of the virus were likely confounded by the number of introductions into the region early in the epidemic and the interconnectedness of the region as a whole.

Repeated Coronavirus Disease 2019 Molecular Testing: Correlation of Severe Acute Respiratory Syndrome Coronavirus 2 Culture With Molecular Assays and Cycle Thresholds.

BACKGROUND: Repeated coronavirus disease 2019 (COVID-19) molecular testing can lead to positive test results after negative results and to multiple positive results over time. The association between positive test results and infectious virus is important to quantify. METHODS: A 2-month cohort of retrospective data and consecutively collected specimens from patients with COVID-19 or patients under investigation were used to understand the correlation between prolonged viral RNA positive test results, cycle threshold (Ct) values and growth of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in cell culture. Whole-genome sequencing was used to confirm virus genotype in patients with prolonged viral RNA detection. Droplet digital polymerase chain reaction was used to assess the rate of false-negative COVID-19 diagnostic test results. RESULTS: In 2 months, 29 686 specimens were tested and 2194 patients underwent repeated testing. Virus recovery in cell culture was noted in specimens with a mean Ct value of 18.8 (3.4) for SARS-CoV-2 target genes. Prolonged viral RNA shedding was associated with positive virus growth in culture in specimens collected up to 21 days after the first positive result but mostly in individuals symptomatic at the time of sample collection. Whole-genome sequencing provided evidence the same virus was carried over time. Positive test results following negative results had Ct values >29.5 and were not associated with virus culture. Droplet digital polymerase chain reaction results were positive in 5.6% of negative specimens collected from patients with confirmed or clinically suspected COVID-19. CONCLUSIONS: Low Ct values in SARS-CoV-2 diagnostic tests were associated with virus growth in cell culture. Symptomatic patients with prolonged viral RNA shedding can also be infectious.

Genomic Diversity of SARS-CoV-2 During Early Introduction into the United States National Capital Region.

BACKGROUND: The early COVID-19 pandemic has been characterized by rapid global spread. In the United States National Capital Region, over 2,000 cases were reported within three weeks of its first detection in March 2020. We aimed to use genomic sequencing to understand the initial spread of SARS-CoV-2, the virus that causes COVID-19, in the region. By correlating genetic information to disease phenotype, we also aimed to gain insight into any correlation between viral genotype and case severity or transmissibility. METHODS: We performed whole genome sequencing of clinical SARS-CoV-2 samples collected in March 2020 by the Johns Hopkins Health System. We analyzed these regional SARS-CoV-2 genomes alongside detailed clinical metadata and the global phylogeny to understand early establishment of the virus within the region. RESULTS: We analyzed 620 samples from the Johns Hopkins Health System collected between March 11-31, 2020, comprising 37.3% of the total cases in Maryland during this period. We selected 143 of these samples for sequencing, generating 114 complete viral genomes. These genomes belong to all five major Nextstrain-defined clades, suggesting multiple introductions into the region and underscoring the diversity of the regional epidemic. We also found that clinically severe cases had genomes belonging to all of these clades. CONCLUSIONS: We established a pipeline for SARS-CoV-2 sequencing within the Johns Hopkins Health system, which enabled us to capture the significant viral diversity present in the region as early as March 2020. Efforts to control local spread of the virus were likely confounded by the number of introductions into the region early in the epidemic and interconnectedness of the region as a whole.

Isolation and characterization of diverse microbial representatives from the human skin microbiome.

BACKGROUND: The skin micro-environment varies across the body, but all sites are host to microorganisms that can impact skin health. Some of these organisms are true commensals which colonize a unique niche on the skin, while open exposure of the skin to the environment also results in the transient presence of diverse microbes with unknown influences on skin health. Culture-based studies of skin microbiota suggest that skin microbes can affect skin properties, immune responses, pathogen growth, and wound healing. RESULTS: In this work, we greatly expanded the diversity of available commensal organisms by collecting > 800 organisms from 3 body sites of 17 individuals. Our collection includes > 30 bacterial genera and 14 fungal genera, with Staphylococcus and Micrococcus as the most prevalent isolates. We characterized a subset of skin isolates for the utilization of carbon compounds found on the skin surface. We observed that members of the skin microbiota have the capacity to metabolize amino acids, steroids, lipids, and sugars, as well as compounds originating from personal care products. CONCLUSIONS: This collection is a resource that will support skin microbiome research with the potential for discovery of novel small molecules, development of novel therapeutics, and insight into the metabolic activities of the skin microbiota. We believe this unique resource will inform skin microbiome management to benefit skin health. Video abstract.

Complete Genome Sequence of an Influenza D Virus Strain Identified in a Pig with Subclinical Infection in the United States.

Influenza D virus was first described in 2011 from a pig with respiratory disease; however, recent evidence indicates that cattle are the major viral reservoir. Here, we describe the genome sequence of the eighth complete swine-origin influenza D virus deposited into GenBank, D/swine/Kentucky/17TOSU1262/2017, which was collected at a 2017 swine exhibition.

Evolution on the Biophysical Fitness Landscape of an RNA Virus.

Viral evolutionary pathways are determined by the fitness landscape, which maps viral genotype to fitness. However, a quantitative description of the landscape and the evolutionary forces on it remain elusive. Here, we apply a biophysical fitness model based on capsid folding stability and antibody binding affinity to predict the evolutionary pathway of norovirus escaping a neutralizing antibody. The model is validated by experimental evolution in bulk culture and in a drop-based microfluidics that propagates millions of independent small viral subpopulations. We demonstrate that along the axis of binding affinity, selection for escape variants and drift due to random mutations have the same direction, an atypical case in evolution. However, along folding stability, selection and drift are opposing forces whose balance is tuned by viral population size. Our results demonstrate that predictable epistatic tradeoffs between molecular traits of viral proteins shape viral evolution.

Statistical analysis of co-occurrence patterns in microbial presence-absence datasets.

Drawing on a long history in macroecology, correlation analysis of microbiome datasets is becoming a common practice for identifying relationships or shared ecological niches among bacterial taxa. However, many of the statistical issues that plague such analyses in macroscale communities remain unresolved for microbial communities. Here, we discuss problems in the analysis of microbial species correlations based on presence-absence data. We focus on presence-absence data because this information is more readily obtainable from sequencing studies, especially for whole-genome sequencing, where abundance estimation is still in its infancy. First, we show how Pearson's correlation coefficient (r) and Jaccard's index (J)-two of the most common metrics for correlation analysis of presence-absence data-can contradict each other when applied to a typical microbiome dataset. In our dataset, for example, 14% of species-pairs predicted to be significantly correlated by r were not predicted to be significantly correlated using J, while 37.4% of species-pairs predicted to be significantly correlated by J were not predicted to be significantly correlated using r. Mismatch was particularly common among species-pairs with at least one rare species (<10% prevalence), explaining why r and J might differ more strongly in microbiome datasets, where there are large numbers of rare taxa. Indeed 74% of all species-pairs in our study had at least one rare species. Next, we show how Pearson's correlation coefficient can result in artificial inflation of positive taxon relationships and how this is a particular problem for microbiome studies. We then illustrate how Jaccard's index of similarity (J) can yield improvements over Pearson's correlation coefficient. However, the standard null model for Jaccard's index is flawed, and thus introduces its own set of spurious conclusions. We thus identify a better null model based on a hypergeometric distribution, which appropriately corrects for species prevalence. This model is available from recent statistics literature, and can be used for evaluating the significance of any value of an empirically observed Jaccard's index. The resulting simple, yet effective method for handling correlation analysis of microbial presence-absence datasets provides a robust means of testing and finding relationships and/or shared environmental responses among microbial taxa.

Murine norovirus (MNV-1) exposure in vitro to the purine nucleoside analog Ribavirin increases quasispecies diversity.

Ribavirin is a pharmaceutical antiviral used for the treatment of RNA virus infections including norovirus, hepatitis C virus, hepatitis E virus, Lassa virus, respiratory syncytial virus, and rhinovirus. Despite the drug's history and documented efficacy, the antiviral mechanism of Ribavirin remains unclear. Mechanisms proposed include depletion of the intracellular GTP pool, immunomodulatory effects, induction of error catastrophe, inhibition of viral polymerase activity, and/or inhibition of viral capping. In the present study, we leveraged deep sequencing data to demonstrate that Ribavirin increases murine norovirus (MNV-1) viral diversity. By serial passaging MNV-1 in RAW 264.7 cells for twenty generations in the presence of Ribavirin, we demonstrated statistically significant increases in both the number of unique haplotypes and the average pairwise difference (APD). Based on statistically significant differences in the probability of nucleotide mutations based on Roche 454 sequencing, we also demonstrated that single nucleotide substitutions are increased in the presence of Ribavirin. Finally, we demonstrated Ribavirin's impact on statistically significantly reducing the relative proportion of the dominant sequence within the quasispecies.

Isolation and Analysis of Rare Norovirus Recombinants from Coinfected Mice Using Drop-Based Microfluidics.

UNLABELLED: Human noroviruses (HuNoVs) are positive-sense RNA viruses that can cause severe, highly infectious gastroenteritis. HuNoV outbreaks are frequently associated with recombination between circulating strains. Strain genotyping and phylogenetic analyses show that noroviruses often recombine in a highly conserved region near the junction of the viral polyprotein (open reading frame 1 [ORF1]) and capsid (ORF2) genes and occasionally within the RNA-dependent RNA polymerase (RdRP) gene. Although genotyping methods are useful for tracking changes in circulating viral populations, they report only the dominant recombinant strains and do not elucidate the frequency or range of recombination events. Furthermore, the relatively low frequency of recombination in RNA viruses has limited studies to cell culture or in vitro systems, which do not reflect the complexities and selective pressures present in an infected organism. Using two murine norovirus (MNV) strains to model coinfection, we developed a microfluidic platform to amplify, detect, and recover individual recombinants following in vitro and in vivo coinfection. One-step reverse transcriptase PCR (RT-PCR) was performed in picoliter drops with primers that identified the wild-type and recombinant progenies and scanned for recombination breakpoints at ∼1-kb intervals. We detected recombination between MNV strains at multiple loci spanning the viral protease, RdRP, and capsid ORFs and isolated individual recombinant RNA genomes that were present at a frequency of 1/300,000 or higher. This study is the first to examine norovirus recombination following coinfection of an animal and suggests that the exchange of RNA among viral genomes in an infected host occurs in multiple locations and is an important driver of genetic diversity. IMPORTANCE: RNA viruses increase diversity and escape host immune barriers by genomic recombination. Studies using a number of viral systems indicate that recombination occurs via template switching by the virus-encoded RNA-dependent RNA polymerase (RdRP). However, factors that govern the frequency and positions of recombination in an infected organism remain largely unknown. This work leverages advances in the applied physics of drop-based microfluidics to isolate and sequence rare recombinants arising from the coinfection of mice with two distinct strains of murine norovirus. This study is the first to detect and analyze norovirus recombination in an animal model.

A high-throughput drop microfluidic system for virus culture and analysis.

High mutation rates and short replication times lead to rapid evolution in RNA viruses. New tools for high-throughput culture and analysis of viral phenotypes will enable more effective studies of viral evolutionary processes. A water-in-oil drop microfluidic system to study virus-cell interactions at the single event level on a massively parallel scale is described here. Murine norovirus (MNV-1) particles were co-encapsulated with individual RAW 264.7 cells in 65 pL aqueous drops formed by flow focusing in 50 µm microchannels. At low multiplicity of infection (MOI), viral titers increased greatly, reaching a maximum 18 h post-encapsulation. This system was employed to evaluate MNV-1 escape from a neutralizing monoclonal antibody (clone A6.2). Further, the system was validated as a means for testing escape from antibody neutralization using a series of viral point mutants. Finally, the replicative capacity of single viral particles in drops under antibody stress was tested. Under standard conditions, many RNA virus stocks harbor minority populations of genotypic and phenotypic variants, resulting in quasispecies. These data show that when single cells are encapsulated with single viral particles under antibody stress without competition from other virions, the number of resulting infectious particles is nearly equivalent to the number of viral genomes present. These findings suggest that lower fitness virions can infect cells successfully and replicate, indicating that the microfluidics system may serve as an effective tool for isolating mutants that escape evolutionary stressors.

Flexibility in surface-exposed loops in a virus capsid mediates escape from antibody neutralization.

UNLABELLED: New human norovirus strains emerge every 2 to 3 years, partly due to mutations in the viral capsid that allow escape from antibody neutralization and herd immunity. To understand how noroviruses evolve antibody resistance, we investigated the structural basis for the escape of murine norovirus (MNV) from antibody neutralization. To identify specific residues in the MNV-1 protruding (P) domain of the capsid that play a role in escape from the neutralizing monoclonal antibody (MAb) A6.2, 22 recombinant MNVs were generated with amino acid substitutions in the A'B' and E'F' loops. Six mutations in the E'F' loop (V378F, A382K, A382P, A382R, D385G, and L386F) mediated escape from MAb A6.2 neutralization. To elucidate underlying structural mechanisms for these results, the atomic structure of the A6.2 Fab was determined and fitted into the previously generated pseudoatomic model of the A6.2 Fab/MNV-1 virion complex. Previously, two distinct conformations, A and B, of the atomic structures of the MNV-1 P domain were identified due to flexibility in the two P domain loops. A superior stereochemical fit of the A6.2 Fab to the A conformation of the MNV P domain was observed. Structural analysis of our observed escape mutants indicates changes toward the less-preferred B conformation of the P domain. The shift in the structural equilibrium of the P domain toward the conformation with poor structural complementarity to the antibody strongly supports a unique mechanism for antibody escape that occurs via antigen flexibility instead of direct antibody-antigen binding. IMPORTANCE: Human noroviruses cause the majority of all nonbacterial gastroenteritis worldwide. New epidemic strains arise in part by mutations in the viral capsid leading to escape from antibody neutralization. Herein, we identify a series of point mutations in a norovirus capsid that mediate escape from antibody neutralization and determine the structure of a neutralizing antibody. Fitting of the antibody structure into the virion/antibody complex identifies two conformations of the antibody binding domain of the viral capsid: one with a superior fit and the other with an inferior fit to the antibody. These data suggest a unique mode of antibody neutralization. In contrast to other viruses that largely escape antibody neutralization through direct disruption of the antibody-virus interface, we identify mutations that acted indirectly by limiting the conformation of the antibody binding loop in the viral capsid and drive the antibody binding domain into the conformation unable to be bound by the antibody.