Decision letter: Improving pandemic influenza risk assessment

Field: Medicine (Health Sciences)

Article Figures and data Abstract Main text References Decision letter Author response Article and author information Metrics Abstract Assessing the pandemic risk posed by specific non-human influenza A viruses is an important goal in public health research. As influenza virus genome sequencing becomes cheaper, faster, and more readily available, the ability to predict pandemic potential from sequence data could transform pandemic influenza risk assessment capabilities. However, the complexities of the relationships between virus genotype and phenotype make such predictions extremely difficult. The integration of experimental work, computational tool development, and analysis of evolutionary pathways, together with refinements to influenza surveillance, has the potential to transform our ability to assess the risks posed to humans by non-human influenza viruses and lead to improved pandemic preparedness and response. https://doi.org/10.7554/eLife.03883.001 Main text Influenza pandemics arise when antigenically novel influenza viruses enter and spread extensively in the human population. By this definition, there have been five influenza pandemics in the last 100 years, the worst of which cost 50 million lives worldwide (Johnson and Mueller, 2002). Of these pandemics, three likely arose from the introduction of genes from avian viruses into the human population (1918—H1N1, 1957—H2N2, 1968—H3N2 (dos Reis et al., 2009; Neumann et al., 2009, Worobey et al., 2014)), one arose from the introduction of a swine virus (2009—H1N1 (Smith et al., 2009)), and one was likely due to the unintended reintroduction of a previously widespread human virus that had not been seen in humans for two decades (1977—H1N1 (dos Reis et al., 2009, Nakajima et al., 1978, Palese, 2004)). However, the viruses responsible for these pandemics represent only a tiny fraction of the total diversity of influenza A viruses that exist in nature (Webster et al., 1992). Assessing which viruses pose the greatest risk of causing the next human pandemic is an enormous challenge. Pandemic influenza risk assessment faces a fundamental problem: a paucity of empirical data on the differences between pandemic viruses and their immediate ancestors from non-human hosts. The challenge was clearly articulated by Harvey Fineberg in his analysis of the US government's response to the 1976 swine influenza scare (Fineberg, 2009): 'The first lesson is to avoid over-confidence about scientific insights. Major flu pandemics arise on average only about three times every century, which means scientists can make relatively few direct observations in each lifetime and have a long time to think about each observation. That is a circumstance that is ripe for over-interpretation.' Core elements of current approaches to pandemic preparedness and mitigation, such as the development of vaccines and stockpiling of antiviral drugs, require detailed virological and immunological data on viruses with perceived pandemic potential and ample lead time for production (Jennings et al., 2008, Keitel and Piedra, 2014). The substantial diversity of known influenza viruses in non-human hosts, and the frequent identification of new viruses, makes extensive experimental testing and development of pandemic preparedness measures against all viruses unfeasible. Thus, there is a need for continuing attempts to assess the pandemic risks posed by non-human viruses in order to prioritize viruses of concern for pandemic preparedness planning. Currently, influenza pandemic risk assessment is largely driven by a simple idea: animal viruses that cause sporadic human infections are thought to pose a greater pandemic risk than viruses that have not been documented to infect humans (Figure 1). This intuitively attractive idea does not have direct empirical support, as none of the viruses that caused the 1918, 1957, 1968, or 2009 pandemics was detected in humans before they emerged in their pandemic form (Smith et al., 2009). This is largely due to a lack of surveillance (1918, 1957, and 1968 pandemics) and to the mistaken assumption that virus subtypes already circulating in humans were unlikely to cause pandemics (2009 pandemic) (Peiris et al., 2012). However, increased surveillance has probably improved the chance that the next pandemic virus will be identified prior to sustained human-to-human transmission. Figure 1 Download asset Open asset Evidence for concern and actions to mitigate influenza pandemics. Types of evidence that have been, or could be, used to justify specific preparedness or mitigation actions prior to evidence of sustained human-to-human transmission, largely based on the authors' interpretation of national and international responses to H5N1, H7N9, and H3N2v outbreaks (Epperson et al., 2013, WHO, 2011). Red indicates largely sufficient, orange partly sufficient, yellow minimally sufficient, gray insufficient. * high pathogenicity phenotype as defined by the World Organization for Animal Health (OIE)(OIE, 2013). https://doi.org/10.7554/eLife.03883.002 If it is true that influenza surveillance has the possibility of identifying potential pandemic viruses before they begin to spread extensively between humans, then improving the basis for assessment of the risks posed by those viruses is an important goal. The level of public health concern about identified non-human influenza viruses should be a function of the potential of each virus to gain the ability to transmit efficiently from human to human and the severity of disease that such a virus would cause should it become pandemic. These two high-level phenotypes are each determined by the interaction of a number of biochemical traits of the virus during human infection (Figure 2) (Chou et al., 2011, Hatta et al., 2001, Kobasa et al., 2004, Labadie et al., 2007, Yen et al., 2011), the state of immunity to that influenza virus in human populations at the time of emergence (Miller et al., 2010, Xu et al., 2010), and by environmental factors such as temperature and humidity (Shaman et al., 2011). Figure 2 Download asset Open asset Schematic of potential relationships from virus genetic sequence to level of public health concern/pandemic risk. Pandemic risk is a combination of the probability that a virus will cause a pandemic and the human morbidity and mortality that might result from that pandemic. Arrows represent possible relationships between levels and are not intended to summarize current knowledge. https://doi.org/10.7554/eLife.03883.003 Currently, the primary tool that uses multiple data streams for assessing pandemic risk is the Influenza Risk Assessment Tool (IRAT) (Cox et al., 2014, Trock et al., 2012). The IRAT integrates existing knowledge, including information on virus transmissibility and disease severity, with expert opinion about potential pandemic viruses to assign relative risk scores to those viruses. The IRAT is useful for identifying key gaps in knowledge, focusing risk management efforts, and providing clear documentation of decision rationales. However, to be used optimally, the IRAT requires a substantial amount of experimental data about virus phenotypes including information on receptor binding, transmissibility in laboratory animals, and antiviral treatment susceptibility. In the absence of phenotype data, preliminary assessments with the IRAT must rely on extrapolations from related viruses, which are prone to subjective interpretation. The biochemical traits that determine virus phenotypes are themselves determined by the genetic sequence of the virus (Figure 2). In theory, it might eventually be possible to predict virus phenotype directly from virus sequence data. However, the complexities of the relationships between sequences and traits and from traits to disease phenotypes, make the prediction of pandemic potential from genomic sequence a tremendous challenge. Here, we discuss ways in which laboratory experiments, together with computational and theoretical developments, could improve genotype-to-phenotype prediction and, in conjunction with enhanced surveillance, improve assessment of the risks posed to humans by non-human influenza viruses. Experimental approaches One goal of experimental studies on non-human influenza viruses is to identify general virus traits that are likely to affect transmissibility between humans, and then relate those traits to specific virus sequence changes. For obvious reasons, direct experimental assessment of human-to-human transmission of potential pandemic viruses is not feasible. However, influenza viruses that have caused pandemics in humans have been shown to transmit efficiently in animal models (most commonly ferrets) (Chou et al., 2011, Yen et al., 2011), thus animal models are thought to be useful for examining the genetic changes in viruses that facilitate human-to-human transmission. For example, several studies have shown that genetic changes in the neuraminidase (NA) and matrix (M) gene segments acquired by the virus lineage responsible for the 2009 H1N1 pandemic increased transmissibility in animal models (Chou et al., 2011, Lakdawala et al., 2011, Yen et al., 2011), suggesting that these changes may have played a role in enhancing the virus's transmissibility in humans and hence paved the way for pandemic emergence. When animal experiments provide quantitative measures of virus traits, these can be integrated into quantitative measures of risk assessment such as the IRAT (Trock et al., 2012). Recently, several high-profile and controversial gain-of-function (GoF) studies have attempted to go beyond the characterization of existing viruses to prospectively identify new mutations in avian H5N1 viruses that enhance the ability of these viruses to transmit between ferrets by the airborne route (Chen et al., 2012, Herfst et al., 2012, Imai et al., 2012, Zhang et al., 2013). Important questions about the relative risks and benefits of these studies have been debated extensively elsewhere (Fauci, 2012; Fouchier et al., 2013; Lipkin, 2012; Casadevall and Imperiale, 2014; Lipsitch and Galvani, 2014); here, we focus on scientific considerations. Because of the vast size of genetic space, such studies cannot possibly delineate all genetic variants of a virus that might be transmissible—after all, there are more than 1018 different possible five-mutation variants of any given hemagglutinin (HA), which is more than what can reasonably be assayed experimentally and the vast majority will not facilitate transmissibility. A more modest goal is to attempt to associate classes of genetic or phenotypic traits with transmissibility. Transmissibility traits identified by GoF studies to date include some that were already known (such as switching receptor binding from avian-like α2,3 sialic acid to human-like α2,6 sialic acid linkages (Yamada et al., 2006) and lowering the optimal temperature for viral polymerase activity (Massin et al., 2001)), as well as some that are new, such as increasing HA stability and reducing glycosylation on HA's globular head (Herfst et al., 2012, Imai et al., 2012). Whether these traits are either necessary or sufficient for transmissibility among humans or even other mammalian animal models remains unclear. For example, a recent study of an avian H5N1 virus found that by reassorting its internal genes with those of a 2009 pandemic virus, the virus could be rendered transmissible in guinea pigs (which have both α2,6 and α2,3 sialic acid in the upper respiratory tract) despite retaining a preference for binding α2,3 sialic acid. However, when mutations identified in earlier ferret GoF experiments were used to switch the receptor specificity to α2,6 sialic acid, transmissibility was lost (Zhang et al., 2013). A key question for efforts to assess pandemic risk of non-human viruses is the degree to which certain substitutions are general markers for a phenotype, or whether the impacts of those mutations are dependent on genetic context and/or specific non-human host. Some mutations have been shown to be strong markers for phenotype for well-defined collections of viruses—for instance, the NA mutation H275Y consistently confers oseltamivir resistance on N1 neuraminidases (although the impact of the mutation on surface expression of NA, and thus virus fitness, varies dramatically) (Baz et al., 2010, Bloom et al., 2010). Similarly, the PB2 E627K substitution adapts the viral polymerase to mammalian cells in some viruses (Long et al., 2013) but not others (Herfst et al., 2010), while other viruses have adapted to mammals via different substitutions in PB2 (Jagger et al., 2010, Mehle and Doudna, 2009; Zhu et al., 2010). In many cases, the effect of mutations can be highly sensitive to genetic context—for instance, the effects of cytotoxic T-lymphocyte escape mutations on nucleoprotein (NP) function depend on the stability of the parent protein, which can be affected by at least dozens of other mutations (Gong et al., 2013). Similar patterns of context dependence have recently been shown for receptor binding specificity substitutions in H5N1 viruses (Tharakaraman et al., 2013). Therefore, even when phenotypic traits of interest can be identified, clear genetic markers for these traits are only present in some cases. The utility of experimental studies for informing surveillance for higher-risk viruses hinges on the question of whether virus traits associated with risk of infection and transmission in humans possess clear genetic markers. If a trait only arises from a limited number of specific mutations or combination of mutations, then experimentally delineating these mutations would be helpful for surveillance. For these cases, it is important and useful for the community to have access to collections of interpretable genotype to phenotype traits such as in the H5N1 genetic changes inventory (http://www.cdc.gov/flu/avianflu/h5n1-genetic-changes.htm) as well as computational tools to quickly connect new sequences to the body of available mutation annotation knowledge (FluSurver: http://flusurver.bii.a-star.edu.sg/). On the other hand, if a trait can be conferred by a large number of different mutations or combinations of mutations, then it will be less effective to monitor specific mutations. In such cases, it may be more beneficial to focus on the broader biochemical properties of viruses or their proteins. Developing laboratory capacity for rapid phenotype assessment would therefore be a valuable complement to high-throughput sequencing of new viruses. Moving forwards, if such biochemical traits can be clearly delineated and reliably modeled, then computational simulation of proteins could be used to predict phenotype from sequence, even for sequences from viruses that have never been experimentally tested. Computational predictions Computational methods present an attractive adjunct to experimental studies because they have higher throughput, have shorter turnaround times, are cheaper, and are safer than experimental work with whole virulent viruses. The main drawback of computational methods is the largely unknown accuracy of their predictions—a drawback that is exacerbated by the lack of an established framework for validating the accuracy of the numerous computational prediction methods that populate the literature. The elements of influenza pandemic risk assessment that are most amenable to computational prediction are those that correspond to well-defined, quantifiable molecular-scale traits such as receptor-binding preference, antiviral susceptibility, antigenicity of HA and NA, and possibly T-cell epitopes. Higher-level phenotypes such as transmissibility, that integrate phenomena at a range of scales, are not yet sufficiently well understood to be reasonable targets for computational predictions. A variety of computational methods shows promise for genotype-to-phenotype prediction including molecular dynamics simulations that combine high- and low-fidelity models (Amaro et al., 2009) and statistical learning approaches that use protein structure, dynamics, and sequence data to predict the phenotypic consequences of mutation (Kasson et al., 2009). However, better prospective validation of these tools against experimental data, particularly for exploring context dependency of genetic changes, is essential before these tools can be reliably used for informing public health decisions or policymaking (Figure 1). Making substantial progress in the development of computational tools and the assessment of their accuracy will require collaboration between experimental and computational scientists to produce consistent testing and validation data. One possible mechanism to spur cooperation would be a series of regular community assessment exercises similar to Critical Assessment of protein Structure Prediction (CASP) (Moult et al., 2011). In a CASP-like exercise, one or more experimental groups would generate quantitative phenotype data for a set of viruses, for example the relative binding of α2,3-sialoglycans and α2,6-sialoglycans, pH profile of viral activation, or sensitivity to oseltamivir, and challenge computational groups to predict that virus phenotype data from the genetic sequences of the viruses tested. The quantitative experimental data would be held under embargo while the exercise runs. Computational groups would complete predictions for these targets, the experimental data set would then be released, and a meeting would be held to assess the performance of different methods to define avenues for improvement. Ideal experimental data sets for CASP-like exercises include thermophoretic or interferometric measurements of HA binding affinities to α2,3- and α2,6-sialoglycans (Xiong et al., 2013) and multi-method characterizations of viral pH activation shifts for sets of point mutants in HA (Galloway et al., 2013, Thoennes et al., 2008). Reliable computational prediction of biochemical traits from genetic data would be a major accomplishment. However, it should be recognized that further major developments, particularly computational prediction of total virus fitness in new hosts, would still be required for realizing the utility of computational tools in policymaking. Evolutionary theory and modeling In addition to the genotype–phenotype relationship itself, there is a need for better understanding of the evolutionary mechanisms and pathways that allow adaptive mutations controlling host range to appear and rise in frequency. These mechanisms act in reservoir hosts, in intermediate hosts (if any), and in humans or other potential hosts; they also act at multiple scales, as viruses compete for replication within hosts and transmission between hosts (Park et al., 2013, Russell et al., 2012). Developing better phylodynamic model frameworks (Grenfell et al., 2004) for modeling virus host transfer and adaptation will require collaboration between theoreticians and experimentalists. Specific goals would be to determine realistic parameters for mutation/selection processes (Illingworth et al., 2014) and virus population bottlenecks at transmission (Wilker et al., 2013) and to generate high-resolution data sets to test and train mechanistic models. Such data-driven mechanistic models could shed light on additional constraints to virus genetic change, such as fitness valleys that separate virus genotypes adapted to one species or another, or conflicts in selection acting at different biological scales. For example, at the most simple level of understanding of the role of receptor binding, avian to mammalian host switching is often assumed to only require a binary change in receptor specificity from α2,3 to α2,6 sialic acid and to be directly related to binding affinity. However, in addition to the α2,3 and α2,6 linkages, there is a tremendous variety in the structures of oligosaccharides displaying the sialic acids and in the structure of the sialic acids in different avian hosts (Gambaryan et al., 2012, Jourdain et al., 2011). The binding specificity for each receptor variant form may affect the potential for different viruses to cross the species barrier or make the difference between causing severe or only mild disease. Rich experimental data sets that provide insights on such factors will improve the power of evolutionary models to interpret experimental and field data. Surveillance methodology Detection of the genetic changes and phenotypes of concern relies on systematic characterization of influenza viruses circulating in wild and domesticated animal populations. If there are virus traits that correlate with genetic markers to risk in humans, or that can be from genetic sequence data, it could be possible to monitor those markers in surveillance and risk assessments prior to emergence in However, the of existing surveillance is largely substantial by host and and only a of the data in the public (Figure Making non-human influenza surveillance more systematic by statistical analysis to determine levels of by and host species would facilitate the of viruses of concern and also have the potential to facilitate of evolutionary and patterns of virus activity that of potential emergence Figure Download asset Open asset of available influenza virus genetic sequence data in to and swine populations. of worldwide animal population by from the and Organization of the of influenza viruses for which sequence data in public from or swine by of influenza virus sequences are not of influenza virus surveillance surveillance is not readily surveillance, even if may result in and is not in these not sequence every influenza virus and some virological surveillance sequence data in public can and interest viruses of concern such as H5N1 and are large of the and many animal populations for which or surveillance is but animal influenza diversity can be to assessments of surveillance by and host similar to efforts for et al., 2012, 2011, et al., 2010, et al., and et al., would to identify major gaps surveillance is either or unlikely to be sufficient for of viruses of For enhancing surveillance, among these gaps will require substantial in understanding animal host to identify for virus transmission within and among animal Similar efforts are required to better what of the facilitate transmission of viruses between and humans, particularly in animal production and animal and the human biological and factors that of transmission of viruses. 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