nextflu: real-time tracking of seasonal influenza virus evolution in humans

Structures and functions linked to genome-wide adaptation of human influenza A viruses

Human influenza A viruses elicit short-term respiratory infections with considerable mortality and morbidity. While H3N2 viruses circulate for more than 50 years, the recent introduction of pH1N1 viruses presents an excellent opportunity for a comparative analysis of the genome-wide evolutionary forces acting on both subtypes. Here, we inferred patches of sites relevant for adaptation, i.e. being under positive selection, on eleven viral protein structures, from all available data since 1968 and correlated these with known functional properties. Overall, pH1N1 have more patches than H3N2 viruses, especially in the viral polymerase complex, while antigenic evolution is more apparent for H3N2 viruses. In both subtypes, NS1 has the highest patch and patch site frequency, indicating that NS1-mediated viral attenuation of host inflammatory responses is a continuously intensifying process, elevated even in the longtime-circulating subtype H3N2. We confirmed the resistance-causing effects of two pH1N1 changes against oseltamivir in NA activity assays, demonstrating the value of the resource for discovering functionally relevant changes. Our results represent an atlas of protein regions and sites with links to host adaptation, antiviral drug resistance and immune evasion for both subtypes for further study.

Human Influenza A Virus Hemagglutinin Glycan Evolution Follows a Temporal Pattern to a Glycan Limit

Frequent mutation of its major antibody target, the glycoprotein hemagglutinin, ensures that the influenza virus is perennially both a rapidly emerging virus and a major threat to public health. One type of mutation escapes immunity by adding a glycan onto an area of hemagglutinin that many antibodies recognize. This study revealed that these glycan changes follow a simple temporal pattern. Every 5 to 7 years, hemagglutinin adds a new glycan, up to a limit. After this limit is reached, no net additions of glycans occur. Instead, glycans are swapped or lost at longer intervals. Eventually, a pandemic replaces the terminally glycosylated hemagglutinin with a minimally glycosylated one from the animal reservoir, restarting the cycle. This pattern suggests the following: (i) some hemagglutinins are evolved for this decades-long process, which is both defined by and limited by successive glycan addition; and (ii) hemagglutinin's antibody dominance and its capacity for mutations are highly adapted features that allow influenza to outpace our antibody-based immunity.

Human antibody-based immunity to influenza A virus is limited by antigenic drift resulting from amino acid substitutions in the hemagglutinin (HA) head domain. Glycan addition can cause large antigenic changes but is limited by fitness costs to viral replication. Here, we report that glycans are added to H1 and H3 HAs at discrete 5-to-7-year intervals, until they reach a functional glycan limit, after which glycans are swapped at approximately 2-fold-longer intervals. Consistent with this pattern, 2009 pandemic H1N1 added a glycan at residue N162 over the 2015–2016 season, an addition that required two epistatic HA head mutations for complete glycosylation. These strains rapidly replaced H1N1 strains globally, by 2017 dominating H3N2 and influenza B virus strains for the season. The pattern of glycan modulation that we outline should aid efforts for tracing the epidemic potential of evolving human IAV strains.

Lineage: Visualizing Multivariate Clinical Data in Genealogy Graphs

The majority of diseases that are a significant challenge for public and individual heath are caused by a combination of hereditary and environmental factors. In this paper we introduce Lineage, a novel visual analysis tool designed to support domain experts who study such multifactorial diseases in the context of genealogies. Incorporating familial relationships between cases with other data can provide insights into shared genomic variants and shared environmental exposures that may be implicated in such diseases. We introduce a data and task abstraction, and argue that the problem of analyzing such diseases based on genealogical, clinical, and genetic data can be mapped to a multivariate graph visualization problem. The main contribution of our design study is a novel visual representation for tree-like, multivariate graphs, which we apply to genealogies and clinical data about the individuals in these families. We introduce data-driven aggregation methods to scale to multiple families. By designing the genealogy graph layout to align with a tabular view, we are able to incorporate extensive, multivariate attributes in the analysis of the genealogy without cluttering the graph. We validate our designs by conducting case studies with our domain collaborators.

Early season co-circulation of influenza A(H3N2) and B(Yamagata): interim estimates of 2017/18 vaccine effectiveness, Canada, January 2018

Using a test-negative design, we assessed interim vaccine effectiveness (VE) for the 2017/18 epidemic of co-circulating influenza A(H3N2) and B(Yamagata) viruses. Adjusted VE for influenza A(H3N2), driven by a predominant subgroup of clade 3C.2a viruses with T131K + R142K + R261Q substitutions, was low at 17% (95% confidence interval (CI): −14 to 40). Adjusted VE for influenza B was higher at 55% (95% CI: 38 to 68) despite prominent use of trivalent vaccine containing lineage-mismatched influenza B(Victoria) antigen, suggesting cross-lineage protection.

Precision epidemiology for infectious disease control

Advances in genomics and computing are transforming the capacity for the characterization of biological systems, and researchers are now poised for a precision-focused transformation in the way they prepare for, and respond to, infectious diseases. This includes the use of genome-based approaches to inform molecular diagnosis and individual-level treatment regimens. In addition, advances in the speed and granularity of pathogen genome generation have improved the capability to track and understand pathogen transmission, leading to potential improvements in the design and implementation of population-level public health interventions. In this Perspective, we outline several trends that are driving the development of precision epidemiology of infectious disease and their implications for scientists' ability to respond to outbreaks.

Current and future influenza vaccines

Although antiviral drugs and vaccines have reduced the economic and healthcare burdens of influenza, influenza epidemics continue to take a toll. Over the past decade, research on influenza viruses has revealed a potential path to improvement. The clues have come from accumulated discoveries from basic and clinical studies. Now, virus surveillance allows researchers to monitor influenza virus epidemic trends and to accumulate virus sequences in public databases, which leads to better selection of candidate viruses for vaccines and early detection of drug-resistant viruses. Here we provide an overview of current vaccine options and describe efforts directed toward the development of next-generation vaccines. Finally, we propose a plan for the development of an optimal influenza vaccine.

Inferring the global phylodynamics of influenza A/H3N2 viruses in Taiwan

BACKGROUND/PURPOSE

Influenza A/H3N2 viruses are characterized by highly mutated RNA genomes. In this study, we focused on tracing the phylodynamics of Taiwanese strains over the past four decades.

METHODS

All Taiwanese H3N2 HA1 sequences and references were downloaded from public database. A Bayesian skyline plot (BSP) and phylogenetic tree were used to analyze the evolutionary history, and Bayesian phylogeographic analysis was applied to predict the spatiotemporal migrations of influenza outbreaks.

RESULTS

Genetic diversity was found to have peaked near the summer of 2009 in BSP, in addition to the two earlier reported ones in summer of 2005 and 2007. We predicted their spatiotemporal migrations and found the summer epidemic of 2005 from Korea, and 2007 and 2009 from the Western United States. BSP also predicted an elevated genetic diversity in 2015-2017. Quasispecies were found over approximately 20% of the strains included in this time span. In addition, a first-time seen N31S mutation was noted in Taiwan in 2016-2017.

CONCLUSION

We comprehensively investigated the evolutionary history of Taiwanese strains in 1979-2017. An epidemic caution could thus be raised if genetic diversity was found to have peaked. An example showed a newly-discovered cluster in 2016-2017 strains featuring a mutation N31S together with HA-160 quasispecies. Phylogeographic analysis, moreover, provided useful insights in tracing the possible source and migrations of these epidemics around the world. We demonstrated that Asian destinations including Taiwan were the immediate followers, while U.S. continent was predicted the origin of two summer epidemics in 2007 and 2009.

GLUE: a flexible software system for virus sequence data

BACKGROUND

Virus genome sequences, generated in ever-higher volumes, can provide new scientific insights and inform our responses to epidemics and outbreaks. To facilitate interpretation, such data must be organised and processed within scalable computing resources that encapsulate virology expertise. GLUE (Genes Linked by Underlying Evolution) is a data-centric bioinformatics environment for building such resources. The GLUE core data schema organises sequence data along evolutionary lines, capturing not only nucleotide data but associated items such as alignments, genotype definitions, genome annotations and motifs. Its flexible design emphasises applicability to different viruses and to diverse needs within research, clinical or public health contexts.

RESULTS

HCV-GLUE is a case study GLUE resource for hepatitis C virus (HCV). It includes an interactive public web application providing sequence analysis in the form of a maximum-likelihood-based genotyping method, antiviral resistance detection and graphical sequence visualisation. HCV sequence data from GenBank is categorised and stored in a large-scale sequence alignment which is accessible via web-based queries. Whereas this web resource provides a range of basic functionality, the underlying GLUE project can also be downloaded and extended by bioinformaticians addressing more advanced questions.

CONCLUSION

GLUE can be used to rapidly develop virus sequence data resources with public health, research and clinical applications. This streamlined approach, with its focus on reuse, will help realise the full value of virus sequence data.

Evolutionary Virology at 40

RNA viruses are diverse, abundant, and rapidly evolving. Genetic data have been generated from virus populations since the late 1970s and used to understand their evolution, emergence, and spread, culminating in the generation and analysis of many thousands of viral genome sequences. Despite this wealth of data, evolutionary genetics has played a surprisingly small role in our understanding of virus evolution. Instead, studies of RNA virus evolution have been dominated by two very different perspectives, the experimental and the comparative, that have largely been conducted independently and sometimes antagonistically. Here, we review the insights that these two approaches have provided over the last 40 years. We show that experimental approaches using in vitro and in vivo laboratory models are largely focused on short-term intrahost evolutionary mechanisms, and may not always be relevant to natural systems. In contrast, the comparative approach relies on the phylogenetic analysis of natural virus populations, usually considering data collected over multiple cycles of virus-host transmission, but is divorced from the causative evolutionary processes. To truly understand RNA virus evolution it is necessary to meld experimental and comparative approaches within a single evolutionary genetic framework, and to link viral evolution at the intrahost scale with that which occurs over both epidemiological and geological timescales. We suggest that the impetus for this new synthesis may come from methodological advances in next-generation sequencing and metagenomics.

Effectiveness of influenza vaccination on influenza-associated hospitalisations over time among children in Hong Kong: a test-negative case-control study

BACKGROUND

The protection conferred by influenza vaccination is generally thought to last less than a year, necessitating annual revaccination. However, the speed with which influenza vaccine effectiveness might decline during a year is unknown, which is of particular importance for locations with year-round influenza activity. We aimed to assess how influenza vaccine effectiveness changes by time intervals between vaccination and admission to hospital, taking advantage of almost year-round circulation of influenza in Hong Kong.

METHODS

In this test-negative case-control study, we analysed vaccine effectiveness in children (aged 6 months to 17 years) who were admitted to hospital in Hong Kong over 5 consecutive years (2012-17). We included those who were admitted to general wards in four public hospitals in Hong Kong with a fever (≥38°C) and any respiratory symptom, such as runny nose, cough, or sore throat. We used direct immunofluorescence assay and reverse transcription PCR to detect influenza virus infection, and recorded children's influenza immunisation history. We compared characteristics of positive cases and negative controls and examined how vaccine effectiveness changed by time between vaccination and admission to hospital with regression analyses.

FINDINGS

Between Sept 1, 2012, and Aug 31, 2017, we enrolled 15 695 children hospitalised for respiratory infections, including 2500 (15·9%) who tested positive for influenza A or B and 13 195 (84·1%) who tested negative. 159 (6·4%) influenza-positive cases and 1445 (11·0%) influenza-negative cases had been vaccinated. Most vaccinations were done by December of each influenza vaccination season. Influenza-related admissions to hospital occurred year-round, with peaks in January through March in most years and a large summer peak in 2016; pooled vaccine effectiveness for children of all ages was 79% (95% CI 42-92) for September to December, 67% (57-74) for January to April, and 43% (25-57) for May to August. Vaccine effectiveness against influenza A or B was estimated as 79% (95% CI 64-88) within 0·5-2 months of vaccination, 60% (46-71) within >2-4 months, 57% (39-70) within >4-6 months, and 45% (22-61) within >6-9 months. In separate analyses by type and subtype, we estimated that vaccine effectiveness declined by 2-5 percentage points per month.

INTERPRETATION

Influenza vaccine effectiveness decreased during the 9 months after vaccination in children in Hong Kong. Our findings confirm the importance of annual vaccination in children. Influenza vaccines that provide broader and longer-lasting protection are needed to provide year-round protection in regions with irregular influenza seasonality or lengthy periods of influenza activity.

FUNDING

Health and Medical Research Fund, Hong Kong and the Research Grants Council, Hong Kong.

From Theory to Practice: Translating Whole-Genome Sequencing (WGS) into the Clinic

Hospitals worldwide are facing an increasing incidence of hard-to-treat infections. Limiting infections and providing patients with optimal drug regimens require timely strain identification as well as virulence and drug-resistance profiling. Additionally, prophylactic interventions based on the identification of environmental sources of recurrent infections (e.g., contaminated sinks) and reconstruction of transmission chains (i.e., who infected whom) could help to reduce the incidence of nosocomial infections. WGS could hold the key to solving these issues. However, uptake in the clinic has been slow. Some major scientific and logistical challenges need to be solved before WGS fulfils its potential in clinical microbial diagnostics. In this review we identify major bottlenecks that need to be resolved for WGS to routinely inform clinical intervention and discuss possible solutions.

ISU FLUture: a veterinary diagnostic laboratory web-based platform to monitor the temporal genetic patterns of Influenza A virus in swine

Background

Influenza A Virus (IAV) causes respiratory disease in swine and is a zoonotic pathogen. Uncontrolled IAV in swine herds not only affects animal health, it also impacts production through increased costs associated with treatment and prevention efforts. The Iowa State University Veterinary Diagnostic Laboratory (ISU VDL) diagnoses influenza respiratory disease in swine and provides epidemiological analyses on samples submitted by veterinarians.

Description

To assess the incidence of IAV in swine and inform stakeholders, the ISU FLUture website was developed as an interactive visualization tool that allows the exploration of the ISU VDL swine IAV aggregate data in the clinical diagnostic database. The information associated with diagnostic cases has varying levels of completeness and is anonymous, but minimally contains: sample collection date, specimen type, and IAV subtype. Many IAV positive samples are sequenced, and in these cases, the hemagglutinin (HA) sequence and genetic classification are completed. These data are collected and presented on ISU FLUture in near real-time, and more than 6,000 IAV positive diagnostic cases and their epidemiological and evolutionary information since 2003 are presented to date. The database and web interface provides rapid and unique insight into the trends of IAV derived from both large- and small-scale swine farms across the United States of America.

Conclusion

ISU FLUture provides a suite of web-based tools to allow stakeholders to search for trends and correlations in IAV case metadata in swine from the ISU VDL. Since the database infrastructure is updated in near real-time and is integrated within a high-volume veterinary diagnostic laboratory, earlier detection is now possible for emerging IAV in swine that subsequently cause vaccination and control challenges. The access to real-time swine IAV data provides a link with the national USDA swine IAV surveillance system and allows veterinarians to make objective decisions regarding the management and control of IAV in swine. The website is publicly accessible at http://influenza.cvm.iastate.edu.

Electronic supplementary material

The online version of this article (10.1186/s12859-018-2408-7) contains supplementary material, which is available to authorized users.

Effective Online Bayesian Phylogenetics via Sequential Monte Carlo with Guided Proposals

Modern infectious disease outbreak surveillance produces continuous streams of sequence data which require phylogenetic analysis as data arrives. Current software packages for Bayesian phylogenetic inference are unable to quickly incorporate new sequences as they become available, making them less useful for dynamically unfolding evolutionary stories. This limitation can be addressed by applying a class of Bayesian statistical inference algorithms called sequential Monte Carlo (SMC) to conduct online inference, wherein new data can be continuously incorporated to update the estimate of the posterior probability distribution. In this article, we describe and evaluate several different online phylogenetic sequential Monte Carlo (OPSMC) algorithms. We show that proposing new phylogenies with a density similar to the Bayesian prior suffers from poor performance, and we develop “guided” proposals that better match the proposal density to the posterior. Furthermore, we show that the simplest guided proposals can exhibit pathological behavior in some situations, leading to poor results, and that the situation can be resolved by heating the proposal density. The results demonstrate that relative to the widely used MCMC-based algorithm implemented in MrBayes, the total time required to compute a series of phylogenetic posteriors as sequences arrive can be significantly reduced by the use of OPSMC, without incurring a significant loss in accuracy.

The WHO global influenza surveillance and response system (GISRS)-A future perspective

In the centenary year of the devastating 1918-19 pandemic, it seems opportune to reflect on the success of the WHO Global Influenza Surveillance and Response System (GISRS) initiated 70 years ago to provide early warning of changes in influenza viruses circulating in the global population to help mitigate the consequences of such a pandemic and maintain the efficacy of seasonal influenza vaccines. Three pandemics later and in the face of pandemic threats from highly pathogenic zoonotic infections by different influenza A subtypes, it continues to represent a model platform for global collaboration and timely sharing of viruses, reagents and information to forestall and respond to public health emergencies.

PROBLEMS OF ISOLATION, IDENTIFICATION AND ANTIGENIC CHARACTERIZATION OF RECENT HUMAN A(H3N2) INFLUENZA VIRUSES

Human A (H3N2) influenza viruses are distinguished by a high rate of evolution and regularly cause epidemics around the world. Their ability to adapt and to escape from the host's immune response and to change their receptor specificity is very high. Over the past 20 years, these viruses have lost the ability to agglutinate red blood cells of chickens and turkeys and have practically ceased to propagate in chicken embryos - the main source of influenza vaccines. Isolation of viruses in the MDCK cell culture led to the selection of strains that lose one of the potential glycosylation sites. Many of the A (H3N2) strains have acquired mutations in neuraminidase, which distort the results of antigenic analysis in the hemagglutination inhibition test - the cornerstone method for the analysis of the match between viral isolates circulating in human population to strains selected for the influenza vaccines. In this regard, the characteristics of the antigenic properties of influenza A (H3N2) viruses by traditional methods become poorly informative, and the selection of vaccine strains of this subtype is erroneous, which is reflected in the discrepancy between vaccine and circulating A (H3N2) viruses in recent years (2013-2014, 2014 -2015, 2015-2016). The search, development and implementation of new algorithms for the isolation and antigen analysis of influenza A (H3N2) viruses are extremely urgent.

Deep mutational scanning of hemagglutinin helps predict evolutionary fates of human H3N2 influenza variants

A key goal in the study of influenza virus evolution is to forecast which viral strains will persist and which ones will die out. Here we experimentally measure the effects of all amino acid mutations to the hemagglutinin protein from a human H3N2 influenza strain on viral growth in cell culture. We show that these measurements have utility for distinguishing among viral strains that do and do not succeed in nature. Overall, our work suggests that new high-throughput experimental approaches may be useful for understanding virus evolution in nature.

Human influenza virus rapidly accumulates mutations in its major surface protein hemagglutinin (HA). The evolutionary success of influenza virus lineages depends on how these mutations affect HA’s functionality and antigenicity. Here we experimentally measure the effects on viral growth in cell culture of all single amino acid mutations to the HA from a recent human H3N2 influenza virus strain. We show that mutations that are measured to be more favorable for viral growth are enriched in evolutionarily successful H3N2 viral lineages relative to mutations that are measured to be less favorable for viral growth. Therefore, despite the well-known caveats about cell-culture measurements of viral fitness, such measurements can still be informative for understanding evolution in nature. We also compare our measurements for H3 HA to similar data previously generated for a distantly related H1 HA and find substantial differences in which amino acids are preferred at many sites. For instance, the H3 HA has less disparity in mutational tolerance between the head and stalk domains than the H1 HA. Overall, our work suggests that experimental measurements of mutational effects can be leveraged to help understand the evolutionary fates of viral lineages in nature—but only when the measurements are made on a viral strain similar to the ones being studied in nature.

History of matrix genes mutations within PCR target regions among circulating influenza H3N2 clades over ten-plus-years

BACKGROUND

Emerging influenza A/H3N2 clades have been associated with M1 gene mutations which affect the performance of commercial PCR assays.

OBJECTIVES AND STUDY DESIGN

The evolution and prevalence of problematic M1 mutations, and their associated viral clades, were investigated. All European and USA isolates from the GISAID database with both HA and M1 sequences available, collected during the respiratory seasons from the Fall of 2007 through January of 2018, were analyzed.

RESULTS

Five M1 target region patterns, designated A-E, were observed in more than 10% of the isolates during a season, with patterns that appeared sequentially, each having one additional mutation. The C153T mutation was universal. Pattern A, which only had the single mutation, predominated between 2007/08 and 2009/10. Dual- and triple-mutation patterns (B and C) emerged in 2010/11 and 2011/12 respectively, and pattern C predominated for one season (2012/13). In 2012/13, the problematic quadruple-mutation containing C163T first appeared in 3C.2 viruses. Seasons 2013/14 and 14/15 were associated with significant viral diversity with five clades and four M1 patterns co-circulating, with different rates in Europe and the USA. Since 2014, clade 3C.2a with M1 pattern D has emerged as the predominant type. During 2016/17 season, a new quintuplet mutation pattern (E) emerged in cluster 3C.2a1 isolates.

CONCLUSIONS

M1 target region mutations have been prevalent for more than ten years, with the number of mutations continually increasing. Often population inferences of M1 mutations can be made based on viral clade. However, gene segment reassortment can affect predictive abilities.

Centennial review of influenza in Taiwan

The history of influenza in Taiwan can be traced up to the 1918 H1N1 Spanish flu pandemic, followed by several others including the 1957 H2N2, 1968 H3N2, and the 2009 new H1N1. A couple of avian influenza viruses of H5N1 and H7N9 also posed threats to the general public in Taiwan in the two recent decades. Nevertheless, two seasonal influenza A viruses and two lineages of influenza B viruses continue causing annual endemics one after the other, or appearing simultaneously. Their interplay provided interesting evolutionary trajectories for these viruses, allowing us to computationally model their global migrations together with the data collected elsewhere from different geographical locations. An island-wide laboratory-based surveillance network was also established since 2000 for systematically collecting and managing the disease and molecular epidemiology. Experiences learned from this network helped in encountering and managing newly emerging infectious diseases, including the 2003 SARS and 2009 H1N1 outbreaks.

Mechanisms and Concepts in RNA Virus Population Dynamics and Evolution

RNA viruses are unique in their evolutionary capacity, exhibiting high mutation rates and frequent recombination. They rapidly adapt to environmental changes, such as shifts in immune pressure or pharmacological challenge. The evolution of RNA viruses has been brought into new focus with the recent developments of genetic and experimental tools to explore and manipulate the evolutionary dynamics of viral populations. These studies have uncovered new mechanisms that enable viruses to overcome evolutionary challenges in the environment and have emphasized the intimate relationship of viral populations with evolution. Here, we review some of the emerging viral and host mechanisms that underlie the evolution of RNA viruses. We also discuss new studies that demonstrate that the relationship between evolutionary dynamics and virus biology spans many spatial and temporal scales, affecting transmission dynamics within and between hosts as well as pathogenesis.

The effects of random taxa sampling schemes in Bayesian virus phylogeography

Public health researchers are often tasked with accurately and quickly identifying the location and time when an epidemic originated from a representative sample of nucleotide sequences. In this paper, we investigate multiple approaches to subsampling the sequence set when employing a Bayesian phylogeographic generalized linear model. Our results indicate that near-categorical posterior MCC estimates on the root can be obtained with replicate runs using 25-50% of the sequence data, and that including 90% of sequences does not necessarily entail more accurate inferences. We present the first analysis of predictor signal suppression and show how the ability to detect the influence of predictor variables is limited when sample size predictors are included in the models.

INSaFLU: an automated open web-based bioinformatics suite "from-reads" for influenza whole-genome-sequencing-based surveillance

BACKGROUND

A new era of flu surveillance has already started based on the genetic characterization and exploration of influenza virus evolution at whole-genome scale. Although this has been prioritized by national and international health authorities, the demanded technological transition to whole-genome sequencing (WGS)-based flu surveillance has been particularly delayed by the lack of bioinformatics infrastructures and/or expertise to deal with primary next-generation sequencing (NGS) data.

RESULTS

We developed and implemented INSaFLU ("INSide the FLU"), which is the first influenza-oriented bioinformatics free web-based suite that deals with primary NGS data (reads) towards the automatic generation of the output data that are actually the core first-line "genetic requests" for effective and timely influenza laboratory surveillance (e.g., type and sub-type, gene and whole-genome consensus sequences, variants' annotation, alignments and phylogenetic trees). By handling NGS data collected from any amplicon-based schema, the implemented pipeline enables any laboratory to perform multi-step software intensive analyses in a user-friendly manner without previous advanced training in bioinformatics. INSaFLU gives access to user-restricted sample databases and projects management, being a transparent and flexible tool specifically designed to automatically update project outputs as more samples are uploaded. Data integration is thus cumulative and scalable, fitting the need for a continuous epidemiological surveillance during the flu epidemics. Multiple outputs are provided in nomenclature-stable and standardized formats that can be explored in situ or through multiple compatible downstream applications for fine-tuned data analysis. This platform additionally flags samples as "putative mixed infections" if the population admixture enrolls influenza viruses with clearly distinct genetic backgrounds, and enriches the traditional "consensus-based" influenza genetic characterization with relevant data on influenza sub-population diversification through a depth analysis of intra-patient minor variants. This dual approach is expected to strengthen our ability not only to detect the emergence of antigenic and drug resistance variants but also to decode alternative pathways of influenza evolution and to unveil intricate routes of transmission.

CONCLUSIONS

In summary, INSaFLU supplies public health laboratories and influenza researchers with an open "one size fits all" framework, potentiating the operationalization of a harmonized multi-country WGS-based surveillance for influenza virus. INSaFLU can be accessed through https://insaflu.insa.pt .

Stochastic processes constrain the within and between host evolution of influenza virus

The evolutionary dynamics of influenza virus ultimately derive from processes that take place within and between infected individuals. Here we define influenza virus dynamics in human hosts through sequencing of 249 specimens from 200 individuals collected over 6290 person-seasons of observation. Because these viruses were collected from individuals in a prospective community-based cohort, they are broadly representative of natural infections with seasonal viruses. Consistent with a neutral model of evolution, sequence data from 49 serially sampled individuals illustrated the dynamic turnover of synonymous and nonsynonymous single nucleotide variants and provided little evidence for positive selection of antigenic variants. We also identified 43 genetically-validated transmission pairs in this cohort. Maximum likelihood optimization of multiple transmission models estimated an effective transmission bottleneck of 1-2 genomes. Our data suggest that positive selection is inefficient at the level of the individual host and that stochastic processes dominate the host-level evolution of influenza viruses.

Treemmer: a tool to reduce large phylogenetic datasets with minimal loss of diversity

BACKGROUND

Large sequence datasets are difficult to visualize and handle. Additionally, they often do not represent a random subset of the natural diversity, but the result of uncoordinated and convenience sampling. Consequently, they can suffer from redundancy and sampling biases.

RESULTS

Here we present Treemmer, a simple tool to evaluate the redundancy of phylogenetic trees and reduce their complexity by eliminating leaves that contribute the least to the tree diversity.

CONCLUSIONS

Treemmer can reduce the size of datasets with different phylogenetic structures and levels of redundancy while maintaining a sub-sample that is representative of the original diversity. Additionally, it is possible to fine-tune the behavior of Treemmer including any kind of meta-information, making Treemmer particularly useful for empirical studies.

Online Bayesian Phylogenetic Inference: Theoretical Foundations via Sequential Monte Carlo

Phylogenetics, the inference of evolutionary trees from molecular sequence data such as DNA, is an enterprise that yields valuable evolutionary understanding of many biological systems. Bayesian phylogenetic algorithms, which approximate a posterior distribution on trees, have become a popular if computationally expensive means of doing phylogenetics. Modern data collection technologies are quickly adding new sequences to already substantial databases. With all current techniques for Bayesian phylogenetics, computation must start anew each time a sequence becomes available, making it costly to maintain an up-to-date estimate of a phylogenetic posterior. These considerations highlight the need for an online Bayesian phylogenetic method which can update an existing posterior with new sequences. Here, we provide theoretical results on the consistency and stability of methods for online Bayesian phylogenetic inference based on Sequential Monte Carlo (SMC) and Markov chain Monte Carlo. We first show a consistency result, demonstrating that the method samples from the correct distribution in the limit of a large number of particles. Next, we derive the first reported set of bounds on how phylogenetic likelihood surfaces change when new sequences are added. These bounds enable us to characterize the theoretical performance of sampling algorithms by bounding the effective sample size (ESS) with a given number of particles from below. We show that the ESS is guaranteed to grow linearly as the number of particles in an SMC sampler grows. Surprisingly, this result holds even though the dimensions of the phylogenetic model grow with each new added sequence.

Epidemiological Studies to Support the Development of Next Generation Influenza Vaccines

The National Institute of Allergy and Infectious Diseases recently published a strategic plan for the development of a universal influenza vaccine. This plan focuses on improving understanding of influenza infection, the development of influenza immunity, and rational design of new vaccines. Epidemiological studies such as prospective, longitudinal cohort studies are essential to the completion of these objectives. In this review, we discuss the contributions of epidemiological studies to our current knowledge of vaccines and correlates of immunity, and how they can contribute to the development and evaluation of the next generation of influenza vaccines. These studies have been critical in monitoring the effectiveness of current influenza vaccines, identifying issues such as low vaccine effectiveness, reduced effectiveness among those who receive repeated vaccination, and issues related to egg adaptation during the manufacturing process. Epidemiological studies have also identified population-level correlates of protection that can inform the design and development of next generation influenza vaccines. Going forward, there is an enduring need for epidemiological studies to continue advancing knowledge of correlates of protection and the development of immunity, to evaluate and monitor the effectiveness of next generation influenza vaccines, and to inform recommendations for their use.

First outbreak of Zika virus in the continental United States: a modelling analysis

Since 2015, Zika virus (ZIKV) has spread throughout Latin and Central America. This emerging infectious disease has been causing considerable public health concern because of severe neurological complications, especially in newborns after congenital infections. In July 2016, the first outbreak in the continental United States was identified in the Wynwood neighbourhood of Miami-Dade County, Florida. In this work, we investigated transmission dynamics using a mathematical model calibrated to observed data on mosquito abundance and symptomatic human infections. We found that, although ZIKV transmission was detected in July 2016, the first importation may have occurred between March and mid-April. The estimated highest value for R₀ was 2.73 (95% confidence interval (CI): 1.65–4.17); the attack rate was 14% (95% CI: 5.6–27.4%), with 15 (95% CI: 6–29) pregnant women involved and a 12% probability of infected blood donations. Vector control avoided 60% of potential infections. According to our results, it is likely that further ZIKV outbreaks identified in other areas of Miami-Dade County were seeded by commuters to Wynwood rather than by additional importation from international travellers. Our study can help prepare future outbreak-related interventions in European areas where competent mosquitoes for ZIKV transmission are already established.

Models for predicting the evolution of influenza to inform vaccine strain selection

Influenza vaccine composition is reviewed before every flu season because influenza viruses constantly evolve through antigenic changes. To inform vaccine updates, laboratories that contribute to the World Health Organization Global Influenza Surveillance and Response System monitor the antigenic phenotypes of circulating viruses all year round. Vaccine strains are selected in anticipation of the upcoming influenza season to allow adequate time for production. A mismatch between vaccine strains and predominant strains in the flu season can significantly reduce vaccine effectiveness. Models for predicting the evolution of influenza based on the relationship of genetic mutations and antigenic characteristics of circulating viruses may inform vaccine strain selection decisions. We review the literature on state-of-the-art tools and prediction methodologies utilized in modeling the evolution of influenza to inform vaccine strain selection. We then discuss areas that are open for improvement and need further research.

Predictive Modeling of Influenza Shows the Promise of Applied Evolutionary Biology

Seasonal influenza is controlled through vaccination campaigns. Evolution of influenza virus antigens means that vaccines must be updated to match novel strains, and vaccine effectiveness depends on the ability of scientists to predict nearly a year in advance which influenza variants will dominate in upcoming seasons. In this review, we highlight a promising new surveillance tool: predictive models. Based on data-sharing and close collaboration between the World Health Organization and academic scientists, these models use surveillance data to make quantitative predictions regarding influenza evolution. Predictive models demonstrate the potential of applied evolutionary biology to improve public health and disease control. We review the state of influenza predictive modeling and discuss next steps and recommendations to ensure that these models deliver upon their considerable biomedical promise.

Influenza interaction with cocirculating pathogens and its impact on surveillance, pathogenesis, and epidemic profile: A key role for mathematical modelling

Evidence is mounting that influenza virus interacts with other pathogens colonising or infecting the human respiratory tract. Taking into account interactions with other pathogens may be critical to determining the real influenza burden and the full impact of public health policies targeting influenza. This is particularly true for mathematical modelling studies, which have become critical in public health decision-making. Yet models usually focus on influenza virus acquisition and infection alone, thereby making broad oversimplifications of pathogen ecology. Herein, we report evidence of influenza virus interactions with bacteria and viruses and systematically review the modelling studies that have incorporated interactions. Despite the many studies examining possible associations between influenza and Streptococcus pneumoniae, Staphylococcus aureus, Haemophilus influenzae, Neisseria meningitidis, respiratory syncytial virus (RSV), human rhinoviruses, human parainfluenza viruses, etc., very few mathematical models have integrated other pathogens alongside influenza. The notable exception is the pneumococcus-influenza interaction, for which several recent modelling studies demonstrate the power of dynamic modelling as an approach to test biological hypotheses on interaction mechanisms and estimate the strength of those interactions. We explore how different interference mechanisms may lead to unexpected incidence trends and possible misinterpretation, and we illustrate the impact of interactions on public health surveillance using simple transmission models. We demonstrate that the development of multipathogen models is essential to assessing the true public health burden of influenza and that it is needed to help improve planning and evaluation of control measures. Finally, we identify the public health, surveillance, modelling, and biological challenges and propose avenues of research for the coming years.

Reconciling Pasteur and Darwin to control infectious diseases

The continual emergence of new pathogens and the increased spread of antibiotic resistance in bacterial populations remind us that microbes are living entities that evolve at rates that impact public health interventions. Following the historical thread of the works of Pasteur and Darwin shows how reconciling clinical microbiology, ecology, and evolution can be instrumental to understanding pathology, developing new therapies, and prolonging the efficiency of existing ones.

Sweep Dynamics (SD) plots: Computational identification of selective sweeps to monitor the adaptation of influenza A viruses

Monitoring changes in influenza A virus genomes is crucial to understand its rapid evolution and adaptation to changing conditions e.g. establishment within novel host species. Selective sweeps represent a rapid mode of adaptation and are typically observed in human influenza A viruses. We describe Sweep Dynamics (SD) plots, a computational method combining phylogenetic algorithms with statistical techniques to characterize the molecular adaptation of rapidly evolving viruses from longitudinal sequence data. SD plots facilitate the identification of selective sweeps, the time periods in which these occurred and associated changes providing a selective advantage to the virus. We studied the past genome-wide adaptation of the 2009 pandemic H1N1 influenza A (pH1N1) and seasonal H3N2 influenza A (sH3N2) viruses. The pH1N1 influenza virus showed simultaneous amino acid changes in various proteins, particularly in seasons of high pH1N1 activity. Partially, these changes resulted in functional alterations facilitating sustained human-to-human transmission. In the evolution of sH3N2 influenza viruses, we detected changes characterizing vaccine strains, which were occasionally revealed in selective sweeps one season prior to the WHO recommendation. Taken together, SD plots allow monitoring and characterizing the adaptive evolution of influenza A viruses by identifying selective sweeps and their associated signatures.

TreeTime: Maximum-likelihood phylodynamic analysis

Mutations that accumulate in the genome of cells or viruses can be used to infer their evolutionary history. In the case of rapidly evolving organisms, genomes can reveal their detailed spatiotemporal spread. Such phylodynamic analyses are particularly useful to understand the epidemiology of rapidly evolving viral pathogens. As the number of genome sequences available for different pathogens has increased dramatically over the last years, phylodynamic analysis with traditional methods becomes challenging as these methods scale poorly with growing datasets. Here, we present TreeTime, a Python-based framework for phylodynamic analysis using an approximate Maximum Likelihood approach. TreeTime can estimate ancestral states, infer evolution models, reroot trees to maximize temporal signals, estimate molecular clock phylogenies and population size histories. The runtime of TreeTime scales linearly with dataset size.

Towards a genomics-informed, real-time, global pathogen surveillance system

The recent Ebola and Zika epidemics demonstrate the need for the continuous surveillance, rapid diagnosis and real-time tracking of emerging infectious diseases. Fast, affordable sequencing of pathogen genomes - now a staple of the public health microbiology laboratory in well-resourced settings - can affect each of these areas. Coupling genomic diagnostics and epidemiology to innovative digital disease detection platforms raises the possibility of an open, global, digital pathogen surveillance system. When informed by a One Health approach, in which human, animal and environmental health are considered together, such a genomics-based system has profound potential to improve public health in settings lacking robust laboratory capacity.

In Silico Vaccine Strain Prediction for Human Influenza Viruses

Vaccines preventing seasonal influenza infections save many lives every year; however, due to rapid viral evolution, they have to be updated frequently to remain effective. To identify appropriate vaccine strains, the World Health Organization (WHO) operates a global program that continually generates and interprets surveillance data. Over the past decade, sophisticated computational techniques, drawing from multiple theoretical disciplines, have been developed that predict viral lineages rising to predominance, assess their suitability as vaccine strains, link genetic to antigenic alterations, as well as integrate and visualize genetic, epidemiological, structural, and antigenic data. These could form the basis of an objective and reproducible vaccine strain-selection procedure utilizing the complex, large-scale data types from surveillance. To this end, computational techniques should already be incorporated into the vaccine-selection process in an independent, parallel track, and their performance continuously evaluated.

Quantifying connectivity between local Plasmodium falciparum malaria parasite populations using identity by descent

With the rapidly increasing abundance and accessibility of genomic data, there is a growing interest in using population genetic approaches to characterize fine-scale dispersal of organisms, providing insight into biological processes across a broad range of fields including ecology, evolution and epidemiology. For sexually recombining haploid organisms such as the human malaria parasite P. falciparum, however, there have been no systematic assessments of the type of data and methods required to resolve fine scale connectivity. This analytical gap hinders the use of genomics for understanding local transmission patterns, a crucial goal for policy makers charged with eliminating this important human pathogen. Here we use data collected from four clinics with a catchment area spanning approximately 120 km of the Thai-Myanmar border to compare the ability of divergence (FST) and relatedness based on identity by descent (IBD) to resolve spatial connectivity between malaria parasites collected from proximal clinics. We found no relationship between inter-clinic distance and FST, likely due to sampling of highly related parasites within clinics, but a significant decline in IBD-based relatedness with increasing inter-clinic distance. This association was contingent upon the data set type and size. We estimated that approximately 147 single-infection whole genome sequenced parasite samples or 222 single-infection parasite samples genotyped at 93 single nucleotide polymorphisms (SNPs) were sufficient to recover a robust spatial trend estimate at this scale. In summary, surveillance efforts cannot rely on classical measures of genetic divergence to measure P. falciparum transmission on a local scale. Given adequate sampling, IBD-based relatedness provides a useful alternative, and robust trends can be obtained from parasite samples genotyped at approximately 100 SNPs.

Predicting the Evolutionary Variability of the Influenza A Virus

The influenza A virus remains one of the most common and dangerous human health concerns due to its rapid evolutionary dynamics. Since the evolutionary changes of influenza A viruses can be traced in real time, the last decade has seen a surge in research on influenza A viruses due to an increase in experimental data (selection of escape mutants followed by examination of their phenotypic characteristics and generation of viruses with desired mutations using reverse genetics). Moreover, the advances in our understanding are also attributable to the development of new computational methods based on a phylogenetic analysis of influenza virus strains and mathematical (integro-differential equations, statistical methods, probability-theory-based methods) and simulation modeling. Continuously evolving highly pathogenic influenza A viruses are a serious health concern which necessitates a coupling of theoretical and experimental approaches to predict the evolutionary trends of the influenza A virus, with a focus on the H5 subtype.

Complete mapping of viral escape from neutralizing antibodies

Identifying viral mutations that confer escape from antibodies is crucial for understanding the interplay between immunity and viral evolution. We describe a high-throughput approach to quantify the selection that monoclonal antibodies exert on all single amino-acid mutations to a viral protein. This approach, mutational antigenic profiling, involves creating all replication-competent protein variants of a virus, selecting with antibody, and using deep sequencing to identify enriched mutations. We use mutational antigenic profiling to comprehensively identify mutations that enable influenza virus to escape four monoclonal antibodies targeting hemagglutinin, and validate key findings with neutralization assays. We find remarkable mutation-level idiosyncrasy in antibody escape: for instance, at a single residue targeted by two antibodies, some mutations escape both antibodies while other mutations escape only one or the other. Because mutational antigenic profiling rapidly maps all mutations selected by an antibody, it is useful for elucidating immune specificities and interpreting the antigenic consequences of viral genetic variation.

Phylogenetic relationships of the HA and NA genes between vaccine and seasonal influenza A(H3N2) strains in Korea

Seasonal influenza is caused by two influenza A subtype (H1N1 and H3N2) and two influenza B lineage (Victoria and Yamagata) viruses. Of these antigenically distinct viruses, the H3N2 virus was consistently detected in substantial proportions in Korea during the 2010/11-2013/14 seasons when compared to the other viruses and appeared responsible for the influenza-like illness rate peak during the first half of the 2011/12 season. To further scrutinize possible causes for this, we investigated the evolutionary and serological relationships between the vaccine and Korean H3N2 strains during the 2011/12 season for the main antigenic determinants of influenza viruses, the hemagglutinin (HA) and neuraminidase (NA) genes. In the 2011/12 season, when the number of H3N2 cases peaked, the majority of the Korean strains did not belong to the HA clade of A/Perth/16/2009 vaccine, and no Korean strains were of this lineage in the NA segment. In a serological assay, post-vaccinated human sera exhibited much reduced hemagglutination inhibition antibody titers against the non-vaccine clade Korean H3N2 strains. Moreover, Korean strains harbored several amino acid differences in the HA antigenic sites and in the NA with respect to vaccine lineages during this season. Of these, the HA antigenic site C residues 45 and 261 and the NA residue 81 appeared to be the signatures of positive selection. In subsequent seasons, when H3N2 cases were lower, the HA and NA genes of vaccine and Korean strains were more phylogenetically related to each other. Combined, our results provide indirect support for using phylogenetic clustering patterns of the HA and possibly also the NA genes in the selection of vaccine viruses and the assessment of vaccine effectiveness.

Interim estimates of 2016/17 vaccine effectiveness against influenza A(H3N2), Canada, January 2017

Using a test-negative design, the Canadian Sentinel Practitioner Surveillance Network (SPSN) assessed interim 2016/17 influenza vaccine effectiveness (VE) against dominant influenza A(H3N2) viruses considered antigenically matched to the clade 3C.2a vaccine strain. Sequence analysis revealed substantial heterogeneity in emerging 3C.2a1 variants by province and over time. Adjusted VE was 42% (95% confidence interval: 18-59%) overall, with variation by province. Interim virological and VE findings reported here warrant further investigation to inform potential vaccine reformulation.

Improving the selection and development of influenza vaccine viruses - Report of a WHO informal consultation on improving influenza vaccine virus selection, Hong Kong SAR, China, 18-20 November 2015

Since 2010 the WHO has held a series of informal consultations to explore ways of improving the currently highly complex and time-pressured influenza vaccine virus selection and development process. In November 2015 experts from around the world met to review the current status of efforts in this field.

Discussion topics included strengthening influenza surveillance activities to increase the availability of candidate vaccine viruses and improve the extent, timeliness and quality of surveillance data. Consideration was also given to the development and potential application of newer laboratory assays to better characterize candidate vaccine viruses, the potential importance of antibodies directed against influenza virus neuraminidase, and the role of vaccine effectiveness studies. Advances in next generation sequencing and whole genome sequencing of influenza viruses were also discussed, along with associated developments in synthetic genomics technologies, evolutionary analysis and predictive mathematical modelling.

Discussions were also held on the late emergence of an antigenic variant influenza A(H3N2) virus in mid-2014 that could not be incorporated in time into the 2014–15 northern hemisphere vaccine. There was broad recognition that given the current highly constrained influenza vaccine development and production timeline it would remain impossible to incorporate any variant virus which emerged significantly long after the relevant WHO biannual influenza vaccine composition meetings. Discussions were also held on the development of pandemic and broadly protective vaccines, and on associated regulatory and manufacturing requirements and constraints.

With increasing awareness of the health and economic burdens caused by seasonal influenza, the ever-present threat posed by zoonotic influenza viruses, and the significant impact of the 2014–15 northern hemisphere seasonal influenza vaccine mismatch, this consultation provided a very timely opportunity to share developments and exchange views. In all areas, a renewed and strengthened emphasis was placed on developing concrete and measurable actions and identifying the key stakeholders responsible for their implementation.

Data, disease and diplomacy: GISAID's innovative contribution to global health

The international sharing of virus data is critical for protecting populations against lethal infectious disease outbreaks. Scientists must rapidly share information to assess the nature of the threat and develop new medical countermeasures. Governments need the data to trace the extent of the outbreak, initiate public health responses, and coordinate access to medicines and vaccines. Recent outbreaks suggest, however, that the sharing of such data cannot be taken for granted - making the timely international exchange of virus data a vital global challenge. This article undertakes the first analysis of the Global Initiative on Sharing All Influenza Data as an innovative policy effort to promote the international sharing of genetic and associated influenza virus data. Based on more than 20 semi-structured interviews conducted with key informants in the international community, coupled with analysis of a wide range of primary and secondary sources, the article finds that the Global Initiative on Sharing All Influenza Data contributes to global health in at least five ways: (1) collating the most complete repository of high-quality influenza data in the world; (2) facilitating the rapid sharing of potentially pandemic virus information during recent outbreaks; (3) supporting the World Health Organization's biannual seasonal flu vaccine strain selection process; (4) developing informal mechanisms for conflict resolution around the sharing of virus data; and (5) building greater trust with several countries key to global pandemic preparedness.

Antibodies Against the Current Influenza A(H1N1) Vaccine Strain Do Not Protect Some Individuals From Infection With Contemporary Circulating Influenza A(H1N1) Virus Strains

During the 2013-2014 influenza season, nearly all circulating 2009 pandemic influenza A(H1N1) virus (A[H1N1]pdm09) strains possessed an antigenically important mutation in hemagglutinin (K166Q). Here, we performed hemagglutination-inhibition (HAI) assays, using sera collected from 382 individuals prior to the 2013-2014 season, and we determined whether HAI titers were associated with protection from A(H1N1)pdm09 infection. Protection was associated with HAI titers against an A(H1N1)pdm09 strain possessing the K166Q mutation but not with HAI titers against the current A(H1N1)pdm09 vaccine strain, which lacks this mutation. These data indicate that contemporary A(H1N1)pdm09 strains are antigenically distinct from the current A(H1N1)pdm09 vaccine strain.

Host immunity and pathogen diversity: A computational study

The distinctive features of human influenza A phylogeny have inspired many mathematical and computational studies of viral infections spreading in a host population, but our understanding of the mechanisms that shape the coupled evolution of host immunity, disease incidence and viral antigenic properties is far from complete. In this paper we explore the epidemiology and the phylogeny of a rapidly mutating pathogen in a host population with a weak immune response, that allows re-infection by the same strain and provides little cross-immunity. We find that mutation generates explosive diversity and that, as diversity grows, the system is driven to a very high prevalence level. This is in stark contrast with the behavior of similar models where mutation gives rise to a large epidemic followed by disease extinction, under the assumption that infection with a strain provides lifelong immunity. For low mutation rates, the behavior of the system shows the main qualitative features of influenza evolution. Our results highlight the importance of heterogeneity in the human immune response for understanding influenza A phenomenology. They are meant as a first step toward computationally affordable, individual based models including more complex host-pathogen interactions.

Rules of co-occurring mutations characterize the antigenic evolution of human influenza A/H3N2, A/H1N1 and B viruses

BACKGROUND

The human influenza viruses undergo rapid evolution (especially in hemagglutinin (HA), a glycoprotein on the surface of the virus), which enables the virus population to constantly evade the human immune system. Therefore, the vaccine has to be updated every year to stay effective. There is a need to characterize the evolution of influenza viruses for better selection of vaccine candidates and the prediction of pandemic strains. Studies have shown that the influenza hemagglutinin evolution is driven by the simultaneous mutations at antigenic sites. Here, we analyze simultaneous or co-occurring mutations in the HA protein of human influenza A/H3N2, A/H1N1 and B viruses to predict potential mutations, characterizing the antigenic evolution.

METHODS

We obtain the rules of mutation co-occurrence using association rule mining after extracting HA1 sequences and detect co-mutation sites under strong selective pressure. Then we predict the potential drifts with specific mutations of the viruses based on the rules and compare the results with the "observed" mutations in different years.

RESULTS

The sites under frequent mutations are in antigenic regions (epitopes) or receptor binding sites.

CONCLUSIONS

Our study demonstrates the co-occurring site mutations obtained by rule mining can capture the evolution of influenza viruses, and confirms that cooperative interactions among sites of HA1 protein drive the influenza antigenic evolution.

The evolution of Ebola virus: Insights from the 2013-2016 epidemic

The 2013-2016 epidemic of Ebola virus disease in West Africa was of unprecedented magnitude and changed our perspective on this lethal but sporadically emerging virus. This outbreak also marked the beginning of large-scale real-time molecular epidemiology. Here, we show how evolutionary analyses of Ebola virus genome sequences provided key insights into virus origins, evolution and spread during the epidemic. We provide basic scientists, epidemiologists, medical practitioners and other outbreak responders with an enhanced understanding of the utility and limitations of pathogen genomic sequencing. This will be crucially important in our attempts to track and control future infectious disease outbreaks.

The Molecular Determinants of Antibody Recognition and Antigenic Drift in the H3 Hemagglutinin of Swine Influenza A Virus

Influenza A virus (IAV) of the H3 subtype is an important respiratory pathogen that affects both humans and swine. Vaccination to induce neutralizing antibodies against the surface glycoprotein hemagglutinin (HA) is the primary method used to control disease. However, due to antigenic drift, vaccine strains must be periodically updated. Six of the 7 positions previously identified in human seasonal H3 (positions 145, 155, 156, 158, 159, 189, and 193) were also indicated in swine H3 antigenic evolution. To experimentally test the effect on virus antigenicity of these 7 positions, substitutions were introduced into the HA of an isogenic swine lineage virus. We tested the antigenic effect of these introduced substitutions by using hemagglutination inhibition (HI) data with monovalent swine antisera and antigenic cartography to evaluate the antigenic phenotype of the mutant viruses. Combinations of substitutions within the antigenic motif caused significant changes in antigenicity. One virus mutant that varied at only two positions relative to the wild type had a >4-fold reduction in HI titers compared to homologous antisera. Potential changes in pathogenesis and transmission of the double mutant were evaluated in pigs. Although the double mutant had virus shedding titers and transmissibility comparable to those of the wild type, it caused a significantly lower percentage of lung lesions. Elucidating the antigenic effects of specific amino acid substitutions at these sites in swine H3 IAV has important implications for understanding IAV evolution within pigs as well as for improved vaccine development and control strategies in swine.

IMPORTANCE

A key component of influenza virus evolution is antigenic drift mediated by the accumulation of amino acid substitutions in the hemagglutinin (HA) protein, resulting in escape from prior immunity generated by natural infection or vaccination. Understanding which amino acid positions of the HA contribute to the ability of the virus to avoid prior immunity is important for understanding antigenic evolution and informs vaccine efficacy predictions based on the genetic sequence data from currently circulating strains. Following our previous work characterizing antigenic phenotypes of contemporary wild-type swine H3 influenza viruses, we experimentally validated that substitutions at 6 amino acid positions in the HA protein have major effects on antigenicity. An improved understanding of the antigenic diversity of swine influenza will facilitate a rational approach for selecting more effective vaccine components to control the circulation of influenza in pigs and reduce the potential for zoonotic viruses to emerge.

Forecasting Epidemiological and Evolutionary Dynamics of Infectious Diseases

Mathematical models have been powerful tools in developing mechanistic understanding of infectious diseases. Furthermore, they have allowed detailed forecasting of epidemiological phenomena such as outbreak size, which is of considerable public-health relevance. The short generation time of pathogens and the strong selection they are subjected to (by host immunity, vaccines, chemotherapy, etc.) mean that evolution is also a key driver of infectious disease dynamics. Accurate forecasting of pathogen dynamics therefore calls for the integration of epidemiological and evolutionary processes, yet this integration remains relatively rare. We review previous attempts to model and predict infectious disease dynamics with or without evolution and discuss major challenges facing the development of the emerging science of epidemic forecasting.

Genomic Analysis of Viral Outbreaks

Genomic analysis is a powerful tool for understanding viral disease outbreaks. Sequencing of viral samples is now easier and cheaper than ever before and can supplement epidemiological methods by providing nucleotide-level resolution of outbreak-causing pathogens. In this review, we describe methods used to answer crucial questions about outbreaks, such as how they began and how a disease is transmitted. More specifically, we explain current techniques for viral sequencing, phylogenetic analysis, transmission reconstruction, and evolutionary investigation of viral pathogens. By detailing the ways in which genomic data can help us understand viral disease outbreaks, we aim to provide a resource that will facilitate the response to future outbreaks.

Sequence amplification via cell passaging creates spurious signals of positive adaptation in influenza virus H3N2 hemagglutinin

Clinical influenza A virus isolates are frequently not sequenced directly. Instead, a majority of these isolates (~70% in 2015) are first subjected to passaging for amplification, most commonly in non-human cell culture. Here, we find that this passaging leaves distinct signals of adaptation, which can confound evolutionary analyses of the viral sequences. We find distinct patterns of adaptation to Madin-Darby (MDCK) and monkey cell culture absent from unpassaged hemagglutinin sequences. These patterns also dominate pooled datasets not separated by passaging type, and they increase in proportion to the number of passages performed. By contrast, MDCK-SIAT1 passaged sequences seem mostly (but not entirely) free of passaging adaptations. Contrary to previous studies, we find that using only internal branches of influenza virus phylogenetic trees is insufficient to correct for passaging artifacts. These artifacts can only be safely avoided by excluding passaged sequences entirely from subsequent analysis. We conclude that future influenza virus evolutionary analyses should appropriately control for potentially confounding effects of passaging adaptations.