Influenza viruses undergo continual antigenic evolution allowing mutant viruses to evade host immunity acquired to previous virus strains. Antigenic phenotype is often assessed through pairwise measurement of cross-reactivity between influenza strains using the hemagglutination inhibition (HI) assay. Here, we extend previous approaches to antigenic cartography, and simultaneously characterize antigenic and genetic evolution by modeling the diffusion of antigenic phenotype over a shared virus phylogeny. Using HI data from influenza lineages A/H3N2, A/H1N1, B/Victoria and B/Yamagata, we determine patterns of antigenic drift across viral lineages, showing that A/H3N2 evolves faster and in a more punctuated fashion than other influenza lineages. We also show that year-to-year antigenic drift appears to drive incidence patterns within each influenza lineage. This work makes possible substantial future advances in investigating the dynamics of influenza and other antigenically-variable pathogens by providing a model that intimately combines molecular and antigenic evolution.DOI:http://dx.doi.org/10.7554/eLife.01914.001 Every year, seasonal influenza, commonly called flu, infects up to one in five people around the world, and causes up to half a million deaths. Even though the human immune system can detect and destroy the virus that causes influenza, people can catch flu many times throughout their lifetimes because the virus keeps evolving in an effort to avoid the immune system. This antigenic drift—so-called because the antigens displayed by the virus keep changing—also explains why influenza vaccines become less effective over time and need to be reformulated every year.It is possible to determine which antigens are displayed by a new strain of the virus by observing how blood samples that respond to known strains respond to the new strain. This information about the “antigenic phenotype” of the virus can be plotted on an antigenic map in which strains with similar antigens cluster together. Gene sequencing has shown that there are four subtypes of the flu virus that commonly infect people; but the relationship between changes in antigenic phenotype and changes in gene sequences of the influenza virus is poorly understood.Bedford et al. have now developed an approach to combine antigenic maps with genetic information about the four subtypes of the human flu virus. This revealed that the antigenic phenotype of H3N2—a subtype that is becoming increasingly common—evolved faster than the other three subtypes. Further, a correlation was observed between antigenic drift and the number of new influenza cases per year for each flu strain. This suggests that knowing which antigenic phenotypes are present at the start of flu season could help predict which strains of the virus will predominate later on.The work of Bedford et al. provides a useful framework to study influenza, and could help to pinpoint which changes in viral genes cause the changes in antigens. This information could potentially speed up the development of new flu vaccines for each flu season.DOI:http://dx.doi.org/10.7554/eLife.01914.002 Seasonal influenza infects between 10% and 20% of the human population every year, causing an estimated 250,000 to 500,000 deaths annually . AlthougBefore 2009, there were four major lineages of influenza circulating within the human population: the H3N2 and H1N1 subtypes of influenza A, and the Victoria and Yamagata lineages of influenza B. In the case of influenza A, subtypes A/H3N2 and A/H1N1 refer to the genes, hemagglutinin (H or HA) and neuraminidase (N or NA), that are primarily responsible for the antigenic character of a strain. In the case of influenza B, Victoria (B/Vic) and Yamagata (B/Yam) refer to antigenically distinct lineages which diverged from a single lineage prior to 1980 . MutatioThe results of many HI assays across a multitude of viruses of a single subtype can be combined to yield a two-dimensional map, quantifying antigenic similarity and distance . The antPreviously, the antigenic and genetic patterns of influenza evolution have been analyzed essentially in isolation. An antigenic map is constructed from a panel of HI measurements, and a phylogenetic tree is constructed from sequence data. However, the opportunity for a combined approach exists as both the antigenic map and the phylogenetic tree often contain many of the same isolates. Here, we implement a flexible Bayesian approach to jointly characterize the antigenic and genetic evolution of the influenza virus population. We apply this approach to investigate the dynamics of A/H3N2, A/H1N1, B/Vic and B/Yam viruses, and, for the first time, present detailed reconstructions of the antigenic dynamics of all four circulating influenza lineages.N-dimensional locations, thus specifying an ‘antigenic map’, such that distances between viruses and sera in this space are inversely proportional to cross-reactivity. In the BMDS model, a map distance of one antigenic unit translates to an expectation of a twofold drop in HI titer between virus and sera. Maps that produce pairwise distances most congruent with the observed titers will have a high likelihood and will be favored by the BMDS model. We integrate over sources of uncertainty, such as antigenic locations, in a flexible Bayesian fashion. We apply this model to HI measurements of virus isolates against post-infection ferret antisera for influenza A/H3N2, A/H1N1, B/Vic and B/Yam.To assess patterns of antigenic evolution among influenza strains, we implemented a Bayesian probabilistic analog of multidimensional scaling (MDS), referred to here as BMDS . In this model, viruses and sera are given N-dimensional locations as described in the ‘Antigenic cartography’ section of ‘Materials and methods’. In this case, ‘serum potencies’ are fixed to the maximum titers exhibited by particular ferret sera and give the baseline expectation for titer when virus and serum are antigenically identical. Potency differs between serum isolates due to experimental noise , but also due to differential ferret immune responses, causing some serum isolates to inhibit hemagglutination at generally higher titers than other isolates. In this model, virus and serum locations follow an uninformative diffuse normal prior. After comparing models of differing dimensions, We begin with Bayesian analogs of the models used by 2 HI titers across influenza lineages , while if HI data is spare, then we expect genetic data to play a larger role in determining antigenic locations (as may be the case for B/Vic and B/Yam).Consequently, we seek to flexibly incorporate genetic data by modeling antigenic phenotype as an evolutionary diffusion , wherein2 HI titers for A/H3N2 when fixing serum potencies and a mean of 10.94 when estimating serum potencies. We find that estimating serum potencies improves test error further , thus supporting our model assumption that the drop in log2 titer is proportional to the Euclidean distance separating viruses and sera on the antigenic map. Additionally, we find that the absolute error in predicted titer is nearly constant with time . Antigenic locations inferred by the model are well resolved; estimates of antigenic distance between pairs of viruses show relatively little variation across the posterior. We estimate that virus distances have, on average, a 50% credible interval of ±0.45 antigenic units for A/H3N2, ±0.57 units for A/H1N1, ±0.76 units for B/Vic, and ±0.65 units for B/Yam.We find that the average absolute error in predicted logWe find strong correspondence between our results and previous results by Through our analysis, we reveal the antigenic, as well as evolutionary, relationships among viruses in influenza A/H3N2, A/H1N1, B/Vic and B/Yam, quantifying both antigenic and evolutionary distances between strains . Over thHI assays lack sensitivity beyond a certain point, so that for A/H3N2, cross-reactive measurements only exist between strains sampled at most 14 years apart, leaving only threshold titers, for example ‘<40’, in more temporally distant comparisons. Because of the threshold of sensitivity of the HI assay, it is difficult to distinguish a linear trajectory in 2D antigenic space from a slightly curved trajectory . To solve this problem of identifiability, we assumed a weak prior that favors linear movement in the 2D antigenic space , so that eτN is measured in terms of years and gives the expected waiting time for two randomly chosen lineages to coalesce in the genealogical tree. We see that eτN broadly correlates with the rate of antigenic drift .We investigate the relationship between rates of antigenic drift and seasonal incidence in the USA in A/H3N2, A/H1N1, B/Vic and B/Yam. We measure seasonal incidence from USA CDC influenza surveillance reports for each virus lineage  by taking the average influenza-like (ILI) percentage in a season and multiplying this by the relative proportion of virus isolations attributable to a particular influenza lineage in a season . This measure of incidence has previously been shown to have have predictive power in the analysis of seasonal influenza trends . We analp-values of 0.056, 0.201, 0.097, and 0.341 respectively. We applied a similar bootstrap permutation test to calculate the significance of finding the observed degree of correlation across all four lineages, arriving at a p-value of 0.018. The fact that we observe periods of pronounced antigenic drift preceding increased incidence in each of the four influenza lineages suggests a causal relationship, in which antigenic evolution drives increased incidence.We follow-up this analysis with a more detailed analysis of year-to-year variation in antigenic drift and lineage-specific incidence, comparing incidence in a season to antigenic drift of viruses coming into this season . For exai and i − 1 against the ratio of the number of isolates from year i to the number of isolates from year i − 1. We found little correlation when combining data across lineages (r = −0.01). We tested significance following the same bootstrap procedure we used to assess the correlation between drift and incidence, finding a p-value of 0.514. Separating lineages gave p-values of 0.717, 0.246, 0.337, 0.504 for A/H3N2, A/H1N1, B/Vic and B/Yam, respectively. These findings suggest our results to be unbiased with regard to sample count.However, it is possible that if sampling count influences cartographic estimates then a spurious correlation could arise in which years with greater incidence have higher sample counts and artifactually high estimates of drift. We controlled for this possibility by testing to see if viral isolate count influences estimates of year-to-year drift by correlating drift between years Understanding antigenic evolution in seasonal influenza is crucial to our efforts of surveillance and control. Cartographic methods allow complex HI datasets to be compressed to more approachable location-based summaries that quantify antigenic relationships between strains, including relationships not directly measured via HI. In this study, we provide a foundation for evolutionary antigenic cartography, which seeks to simultaneously assess antigenic phenotype and antigenic evolution. We use this approach to characterize competitive dynamics across influenza lineages A/H3N2, A/H1N1, B/Vic and B/Yam and show that antigenic evolution within each lineage drives strain replacement and contributes to seasonal incidence patterns. We find that influenza A/H3N2 evolves faster in antigenic phenotype than A/H1N1, which in turn evolves faster than B/Vic or B/Yam. Consequently, the influenza A/H3N2 virus population turns over more quickly than A/H1N1 or influenza B, exhibiting a smaller effective population size and a ‘spindlier’ phylogenetic tree. Furthermore, we observe a correlation between antigenic drift and viral incidence both across and within influenza lineages. The finding that antigenic evolution correlates with subsequent increased incidence within a lineage suggests a causal role for antigenic drift driving influenza incidence patterns. The correlation between incidence and drift further suggests the possibility of using HI data at the start of an influenza season to predict which lineage will subsequently predominate.The statistical framework presented here represents a baseline to which further advancements in modeling antigenic phenotype and evolution may be made. For example, our likelihood-based model facilitates the inclusion of possible covariates affecting immunological titer, which could include experimental factors such as red blood cell type used in the HI assay  and whet2 space and represent the titer of virus i against serum j as Hij = 7.32. Due to experimental constraints, most comparisons cannot be made, leading to a sparse observation matrix Antigenic characteristics of viral strains are often assessed through immunological assays such as the hemagglutination inhibition (HI) assay . At hearPrevious work  has usedP-dimensional antigenic map. One of our initial goals is to find an optimal projection of the high-dimensional distance matrix H into this lower dimensional space. We conduct this projection using Bayesian multidimensional scaling (BMDS)   in whichi for i = 1,…, n, so that P = 2. Similarly, let yj represent the cartographic location of serum j for j = 1,…, k, so that P = 2. For notational compactness, we collect together all virus coordinates into an n × P matrix k × P matrix L2 norm.Let Hij is the log2 titer of virus i against serum j and serum potency X and Y such thatTraditional approaches to antigenic cartography  begin byHere, we instead assume a probabilistic interpretation in which an observed titer is normally distributed around its cartographic expectation with variance dij are directly observed, non-negative quantities. In the antigenic setting, these remain random and can be negative since neither sj is known nor is Hij observed with much precision.Consequently, the likelihood of observing an exact titer given the placement of antigenic locations isHI assays sometimes show no inhibition at all measured titrations, for example a measurement can be reported as ‘<40’. In this case, the likelihood of observing the threshold measurement follows the cumulative density of the lower tail of the normal distribution.These likelihoods are illustrated in We calculate the overall likelihood by multiplying probabilities of individual measurementsj is only measured against distant viruses, its maximum titer will be artificially low and the likelihoods concerning this serum will appear poor. To address this issue, we relax the assumption of fixed sj values and treat the expected log2 titer when Hij still follows s1,…, sk) is random and estimated rather than fixed. We assume that sj values are hierarchically distributed according to a normal distribution. We take an Empirical Bayesian approach in specifying the mean and variance of this distribution, set to the empirical mean and empirical variance of the set of maximum titers across sera The preceding model represents immunological distance as a drop in titer against the most reactive comparison for a particular serum. However, this model may be biased in some circumstances. In one example, if a particular serum vi representing the general level of reactivity across HI assays. With virus avidity included, observed titers followvi for i = 1,…, n is estimated in an analogous hierarchical fashion, with v normally distributed with mean and variance equal to the empirical mean and variance of the set of maximum titers across viruses Additionally, we follow the same logic and assume that some virus isolates are more reactive than other virus isolates and include a parameter for ‘virus avidity’ X and Y will give the same likelihood of an observed data matrix H. An example of this phenomenon is shown in t is the difference between the date of the indexed virus or serum and the date of the earliest sampled virus or serum, and other dimensions follow m ≥ 2. Thus, this model assumes that virus and serum locations drift in a line across the antigenic map at rate μ. The parameter As presented, multiple configurations of virus and serum locations P = 2, where, τ is a phylogeny specifying tree topology and branch lengths. Thus, viruses that are genetically similar are induced to have prior locations close to one another on the antigenic map. In the evolutionary Brownian process, the tips of the phylogeny τ correspond to the set of virus locations , and the probability of observing tip locations depends on the locations of internal nodes  and on the location of the root node xn−22. This process assumes that a virus location xi follows from the location of its parent virus xf(i), and with the addition of drift along dimension 1, is distributed asP = 2, where, f(i) is a function that maps nodes to parental nodes, di is the length of the branch connecting virus i to parent virus f(i), and xn−12 is assumed to follow a normal distribution with expectation P = 2 and variance determined by the diffusion volatility μ only influences the expected states of nodes along the phylogeny, but does not influence the covariance structure among these nodes, which remains the same as it does in a standard Wiener process .We simultaneously model antigenic locations and genetic relatedness by assuming that virus locations are influenced by evolution following a Brownian motion process . To do tility σx . The produced in . This fo process . This alτ is estimated using sequence data for viruses 1,…, n according to well-established methods implemented in the software package BEAST  distributions with a = 0.001 and b = 0.001. These diffuse priors were chosen to be non-informative and provide little-to-no weight on the resulting posterior distributions. Under the full model, the posterior probability of observing virus and serum locations given immunological data is factored.Top-level priors for xi and yj and individual virus avidities vi and serum potencies sj, and other transition kernels that scale the entire set of virus and serum locations X and Y and that scale parameters φ, μ, We sample from this posterior distribution using the MCMC procedures implemented in the software package BEAST . Metropolgorithm .We compiled an antigenic dataset of hemagglutination inhibition (HI) measurements of virus isolates against post-infection ferret sera for influenza A/H3N2 by collecting data from previous publications , NIMR vaAntigenic data for influenza A/H1N1 was collected from previous publications  and NIMRAntigenic comparisons for influenza B/Victoria were collated from previous publications  and vaccAntigenic comparisons for influenza B/Yamagata were collected from previous publications  and vaccSurveillance data was obtained from the Centers of Disease Control and Prevention FluView Influenza Reports from the yearly summaries of influenza seasons 1997–1998 to 2010–2011 . As an ePhylogenetic trees were estimated for A/H3N2, A/H1N1, B/Vic, and B/Yam using BEAST  and incoX, serum locations Y, virus avidities v, serum potencies s, MDS precision μ, virus location precision τ. MCMC chains were run for 500 million steps and parameter values sampled every 200,000 steps after a burn-in of 100 million steps, yielding a total of 2000 MCMC samples. In all cases, when drift parameter μ was included the MCMC chain mixed well and arrived at the same estimated posterior distribution from different starting points. However, without drift parameter μ, maps for A/H3N2 showed some degree of metastability, where some chains would converge on one solution and other chains would converge on a different solution. We favor models that include μ, because its inclusion, in addition to correcting most identifiability issues, yields much improved mixing of antigenic locations.MCMC was used to sample virus locations There is some difficulty in summarizing posterior cartographic samples, as sampled virus and serum locations represent only relative quantities, and because of this, over the course of the MCMC, virus locations may shift. Our prior on virus and serum locations remove much of this issue, orienting the antigenic map along dimension 1 and fixing it to begin at the origin. However, local isometries are often still a problem. For example, in A/H3N2 the HK/68, EN/72 and VI/75 clusters may rotate in relation to other clusters. Consequently, it may be difficult to fully align MCMC samples using Procrustes analysis. For the present study, we take a simple approach and sample a single MCMC step and visualize the antigenic locations at this state . Then, fx and y locations along antigenic dimensions 1 and 2 backward through time. We use MCMC to sample tip locations, but when outputting trees sample internal node locations using a peeling algorithm as described in x and y values at intervals of 0.05 years for each virus. Then, for each interval, x and y values are averaged across the sample of posterior trees. We draw lines between these locations to approximate mean posterior diffusion paths. As virus lineages coalesce backwards through time down the phylogeny these diffusion paths will also coalesce.We summarize diffusion paths of viral lineages  by takin2 drop in HI titer is proportional to Euclidean distance between virus and serum locations. To bring models into further alignment, we use a Uniform  distribution over virus locations and serum locations. Unsurprisingly, we find that this BMDS model produces results that are strongly congruent with MDS results . More details on implementing these models can be found at https://github.com/trvrb/flux/tree/master/example-xmls. Incidence data and HI data used in this analysis is archived with Dryad (doi: 10.5061/dryad.rc515).Source code implementing the cartographic models has been made fully available as part of the software package BEAST , and can review process). Similarly, the author response typically shows only responses to the major concerns raised by the reviewers.eLife posts the editorial decision letter and author response on a selection of the published articles . An edited version of the letter sent to the authors after peer review is shown, indicating the substantive concerns or comments; minor concerns are not usually shown. Reviewers have the opportunity to discuss the decision before the letter is sent In sum, we are concerned that the analysis suggests a bigger connection between drift and incidence than actually exists. It is not clear if there is correlation within clades (these plots were not shown), and the across-clade correlation could be at least partly an artifact of the pooling of clades. In addition, the across-clade and within-clade effects could be amplified by sampling bias. Given, however, the extreme importance of this work to understanding influenza evolution and vaccine selection and the potential to provide a quantitative assessment of the association between drift (as measured by cartography) and incidence, the reviewers and editor would be willing to consider a suitably revised submission with major revisions that convinced us that the correlation is true or that there is perhaps no correlation between drift and incidence (or conceivably that antigenic cartography is not a good way to measure drift).Reviewer #1 comments:This paper is exceptional in many ways, but also contains some flaws that need to be addressed by revisions. If these flaws are addressed, I would be highly supportive of publication.This paper deals with the antigenic evolution of influenza. A foundational paper in Science in 2004 by Smith et al introduced “antigenic cartography,” the technique used to monitor antigenic drift and select the influenza vaccine. This paper makes a clear methodological improvement on the original antigenic cartography technique. The new approach integrates phylogenetics, allowing inclusion of viral sequences and isolation dates. This paper also shows that accounting for virus and serum effects improves cartography. As the authors briefly note, the new method is also extensible to evolutionary questions. All of these improvements are made using a freely available phylogenetic software program that is the standard in the field. These methodological improvements would immediately merit publication in a technique-specific journal.In addition, this paper presents biological results that should interest virologists, public health officials, and evolutionary biologists. While these results do not really alter the current understanding of influenza antigenic evolution, they extend this understanding from H3N2 to other clades, provide a more detailed analysis than has previously been performed for any clade, and quantify factors that have previously been characterized only qualitatively.I do, however, have some major critiques:• It is difficult to understand the biological results without grasping technical aspects of the computational approach. In my specific comments, I suggest some changes.• I do not concur that • The authors need to explain where they got the incidence data, how they chose the date ranges, specify when they are using relative versus absolute incidence, and provide raw data as supporting material. I describe my concerns in more detail in the specific comments.• The improvement from inclusion of serum effects deserves comment and explanation if possible (are the effects due to the serum source)? Table 1 should include a model with virus effects but no serum effects. I describe my concerns in more detail in the specific comments.Major points in more detail:1) I have concerns about 2) The incidence data are poorly explained. In the first paragraph of the Epidemiological consequences of antigenic drift section, the authors use relative incidence. In the next paragraph, are they switching to absolute incidence? This is not clear. If they are switching to absolute incidence, I don't think they ever explain in the Methods where that data comes from. In either case, what are the justifications for using absolute versus relative incidence? Also, why do they not analyze incidence prior to 1998/1999? Finally, because each influenza season spans two calendar years, why do they not break up the data by season rather than year?3) An interesting result is that estimating the serum effects improves the performance a great deal . It suggests that there may be systematic differences between the sera. Can this be attributed to the serum itself (such as whether it is goat versus ferret), or to the particular lab or study? Could the authors correlate the estimated serum effects back to the originating source for that serum to check? As a related point, I was surprised that inclusion of the virus effects led to only minor improvement, since prior work (http://www.ncbi.nlm.nih.gov/pubmed/19900932) suggests that some viruses are generally more resistant to HI than others. However, the virus effects are only included in a model (#9 in Table 1) where the serum effects are already included, and it is possible to imagine that including just one of these terms somehow helps correct for both effects. Can the authors include a tenth model in Table 1 that includes the virus effects but not the serum effects? This would clarify if serum effects are actually more important than virus effects, or simply whether inclusion of either one individually is more helpful than both together.Reviewer #2 comments:The authors extend earlier work on “antigenic cartography” — that is, low-dimensional embeddings of viral strains using dimensionality reduction techniques, in order to quantify antigenic groupings of viral isolates. What is novel here compared to prior work is the introduciton of a Bayesian framework for this mapping process, the observation that antigenic drift within a clade correlates with clade-specific disease incidence each season, and anti-correlates with drift in other clades. The authors also introduce a phenomonological model of antigenic variation based on diffusion, and interpret antigenic drift in terms of fitted diffusion coefficiants. Finally, the authors provide antigenic maps for more clades of influenza  than in previous publications.Understanding antigenic drift of influenza in quantitative terms is clearly of great practical importance, and also an interesting intellectual problem in its own right. And it is nice to see antigenic maps for so many viral clades. However, I do not feel that the technical advances in “mapping” strains introduced here provide us much more confidence in the antigenic locations of strains than prior techniques do. Indeed, the authors themselves note that the average predictive error of this new methods is comparable to that of Smith et al. (2004). What might be more valuable, perhaps, are the results relating inferred antigenic drift and influenza incidence each season, by clade. But these results are quite possibly driven by sampling biases (see below), and I am not really convinced of their validity — at least not without serious efforts to remove such sampling biases. Finally, the discussion of a “diffusion” model of drift was extremely confusing to me, and the fitted diffusion coefficients do not have a clear meaning or interpretation.Serious concerns follow:1) The authors do not seem to discuss a problematic aspect of their results relating inferred drift to influenza disease incidence — namely that biased sampling of clades, in each season, could cause the observed correlations. If clades were sampled for HI assays unequally in each season, as I suspect, then this fact alone might cause the trend towards apparently more drift in the clade that was sampled most intensively in each season (and to which most ILI was attributed). This is an obvious possible confound for the analysis presented in 2) Likewise, in 3) I found the section fitting diffusion coefficients (in different branches) to the antigenic maps confusing — if only because strict diffusion (in one or two dimensions) has no advection term and therefore no tendency to move in one direction or another; whereas the data clearly show an advective tendency (moving to the “right” over time in antigenic dimension 1). I presume the authors attempt to deal with this by imposing a prior (eq. 12) for the location of a virus based on its location in a molecular phylogeny. But this would seem to conflate antigenic drift, which the authors wish to quantify using a diffusion coefficient, with genetic mutations (many of which are driven by selection for antigenic escape). In other words, this hybrid approach of assuming a prior based on a strain's genotype, and then fitting a diffusion coefficient to summarize strength of drift, does not seem to be a pure measure of drift. What exactly the fitted diffusion coefficient means is extremely unclear to me, in any formal sense, as the procedure is so ad-hoc.4) How do the antigenic maps shown compare to BMDS applied to pairwise AA hamming distances alone? How many serious discrepancies that are outside the confidence intervals on map reconstruction? In other words, the authors should demonstrate that the HI data actually provide substantively different results for antigenic grouping than genetic data do alone. This was an important contention of Smith et al. (2004), but it was based on the position of only a handful of viruses in that paper. Do the authors have more convincing evidence in this study that the antigenic data are providing substantial extra information beyond the genetic data?[Editors’ note: what now follows is the decision letter after the authors submitted for further consideration.]eLife. Your submission has been evaluated by a Senior editor and the original two reviewers.Thank you for resubmitting your work entitled “Integrating influenza antigenic dynamics with molecular evolution” for consideration at Both reviewers were pleased with the revised manuscript and only a small number of issues remain to be addressed before acceptance. These are: whether virus effects could be due to avidity, clarifying the meaning of tau in Table 2, expanding on the rationale for subsampling in the Punctuated evolution section, the apparent discrepancy between 1) I like how the authors have included “virus effects” alone in the new Table 1. At least one “virus effect” could be overall receptor avidity, and Plotkin and Hensley have a recent paper  showing that avidity can influence antigenic clustering. It might be worthwhile to include a sentence on the possibility that “virus effects” could be a manifestation of avidity?e * τ. They never define what tau represents, and I don't think it is safe to assume that the reader will know this — I certainly don't. More interpretation here would be helpful.2) In Table 2 and the related discussion, the authors discuss the scaled effective population size N3) In the “Punctuated evolution and its epidemiological consequences” section, the part about subsampling to look at whether drift associates with number of isolates should be expanded. I would suggest starting a new paragraph at the current “We test to see...” sentence that briefly explains the rationale for why this test is being done . I also think that it would be nice to have a table or figure somehow representing the actual results of this analysis.4) 5) My comments where the original reason that the authors removed the argument for the across lineage correlation in incidence. However, now ‘Punctuated evolution and its epidemiological consequences’ section has this orphan paragraph beginning “Although the general correlation between rate of antigenic drift...” This paragraph doesn't seem to make sense in the context of the presented data any more, as the paper no longer has any information about across lineage correlations, so it can't even be seen what they are saying may not be causal. I think this paragraph either needs to be dramatically expended or probably better eliminated. Maybe the previous paragraph could then just get a wrap-up sentence interpreting the results to suggest a strong relationship between drift and incidence within each lineage.6) Although the Conclusion is fine, I feel that it might benefit from a paragraph summarizing the main biological results as regards different rates of drift in lineages and the correlation between drift and incidence within lineages. These biological results are not really mentioned in the current Conclusion. [Editors’ note: the author responses to the first round of peer review follow.]Reviewer #1 comments:1) I have concerns about, which the authors use to support their claim that “antigenic drift drives incidence rates.” The authors first establish the well-known fact that H3N2 incidence is highest, followed by H1N1 and then B. They also find that relative incidence is correlated with the rate of antigenic drift. However, this does not imply causality in terms of the higher average drift driving the higher average incidence. The authors do not claim any causality in this correlation between average drift and incidence in the four clades, and I am therefore fine with this part of the analysis. The problems arise in. The authors find a significant correlation between drift and incidence taken over all years and clades, and then use this to argue that drift drives incidence. I would agree with this conclusion if higher drift was associated with higher incidence with a correlation that is better than that obtained simply by comparing the averages of the four clades. But I don't think this is true. In, the authors are averaging over all clades, making the correlation in 5A a trivial consequence of the fact that H3N2 has higher average incidence and drift. Even if the rate of drift and incidence were constant year-over-year for each clade (and simply differed among clades), the authors would observe the correlation that they report. Put another way; imagine that every year the drift and incidence in each clade was equal to its average. Thenwould in effect correspond to simply recalculating the correlation between average drift and incidence in a scenario where each point is included as many times as there are years being examined. The more years that were included the higher the P-value would get because the number of data points would increase, but really the single actual trend (that H3N2 is higher in average drift and incidence) is just being counted multiple times. In order to draw a conclusion stronger that drift drives incidence, the authors need to show that incidence and drift are correlated within each clade individually, or else standardize the incidence so that the mean and variance for each individual clade is zero and one. It is not clear to me that there is any correlation between the drift and incidence within any of the individual clades. A similar problem affects- for example, since H3N2 has the highest drift, it will always tend to be associated with lower drift in other clades since the other clades inherently have lower average drift. But this does not imply direct interference between clades. I am open to further discussion about this point, but I do not see howimplies that antigenic drift drives incidence, or that there is dynamical interference across clades. By the way, I do not think that a lack of correlation would be a disqualifying concern in terms of publication. Finding no evidence that antigenic drift drives incidence is just as important as finding that it does. But the claim needs to be correct.This is a very astute criticism. We see how differing overall incidence between clades could have given an artifactual signal in our previous year-to-year drift vs incidence comparisons. In this revision, we have reanalyzed the data in the way suggested; we show that year-to-year antigenic drift and incidence are correlated within each clade individually, arriving at correlation coefficients of 0.51, 0.29, 0.44 and 0.14 for A/H3N2, A/H1N1, B/Vic and B/Yam respectively. None of these correlations are significant on their own, however observing four correlation coefficients of this magnitude is highly unlikely under a null model derived from bootstrap permutations (p = 0.018). Because the increase in incidence tends to follow periods of pronounced antigenic drift, we conclude that there appears to be a causal relationship between antigenic change and increased incidence.However, in redoing this analysis on a lineage-by-lineage basis, we lost much of the signal for interference between lineages. We think there may still be something there, but the nuanced analysis that this issue deserves seems beyond the scope of the paper. We have decided to instead drop the discussion of interference between lineages.2) The incidence data are poorly explained. In the first paragraph of the Epidemiological consequences of antigenic drift section, the authors use relative incidence. In the next paragraph, are they switching to absolute incidence? This is not clear. If they are switching to absolute incidence, I don't think they ever explain in the Methods where that data comes from. In either case, what are the justifications for using absolute versus relative incidence?In revising this section in the manuscript, we have made the source of the incidence data and its construction much more clear. In addition, we have removed the relative vs absolute incidence confusion, sticking instead with one measure: ILI × proportion of viral isolates attributable to a lineage.Also, why do they not analyze incidence prior to 1998/1999?The 1998/1999 season is the earliest we have that properly distinguishes B/Vic and B/Yam in the CDC data.Finally, because each influenza season spans two calendar years, why do they not break up the data by season rather than year?This would be much preferable. However, many virus isolates used in this analysis lack temporal resolution beyond the year of sampling. Because of this, we decided to stick with just year of isolation for the present analysis.3) An interesting result is that estimating the serum effects improves the performance a great deal . It suggests that there may be systematic differences between the sera. Can this be attributed to the serum itself (such as whether it is goat versus ferret), or to the particular lab or study? Could the authors correlate the estimated serum effects back to the originating source for that serum to check?We would suggest that a serum with a larger effect contains a more concentrated and active set of antibodies than a serum with a smaller effect. The increased number of antibodies could be due to experimental variation in serum extraction and processing or due to variation in immune response and timing between ferrets, i.e., some ferrets may mount an immediate and strong immune response, while others may mount a weaker response. This causes variation in overall strength of the serum. We try to control for the overall strength when looking at patterns of cross-reactivity through the use of ‘serum effects’. Some differences in serum effects could be tracked down to particular studies, but this work is beyond the scope of the current study. Here, we’ve essentially treated serum effect as a nuisance parameter. We’ve revised the text to discuss this as well as describe why estimating serum effects improves model performance over fixing serum effects.As a related point, I was surprised that inclusion of the virus effects led to only minor improvement, since prior work (http://www.ncbi.nlm.nih.gov/pubmed/19900932) suggests that some viruses are generally more resistant to HI than others. However, the virus effects are only included in a model (#9 in Table 1) where the serum effects are already included, and it is possible to imagine that including just one of these terms somehow helps correct for both effects. Can the authors include a tenth model in Table 1 that includes the virus effects but not the serum effects? This would clarify if serum effects are actually more important than virus effects, or simply whether inclusion of either one individually is more helpful than both together.The inclusion of ‘virus effects’ was meant to capture exactly this observation, that some viruses may be generally more resistant to HI. To further assess their importance, we followed this suggestion and included test error for a model that estimates virus effects, but leaves serum effects fixed at maximum titers. In this case, we observe similar, but not quite as substantial, improvements to test error as the model that estimates serum effects, but does not include virus effects. From this observation, we conclude that estimating either virus or serum effects both improve the model.Reviewer #2 comments:1) The authors do not seem to discuss a problematic aspect of their results relating inferred drift to influenza disease incidence—namely that biased sampling of clades, in each season, could cause the observed correlations. If clades were sampled for HI assays unequally in each season, as I suspect, then this fact alone might cause the trend towards apparently more drift in the clade that was sampled most intensively in each season (and to which most ILI was attributed). This is an obvious possible confound for the analysis presented in, and it should be ruled out by simulation of constant true rates of antigenic drift, variable sampling proportions in each season, and reconstructed drift using BMDS.We tested to see if there might be an bias in the estimates of year-to-year anti- genic drift based on number of virus isolates collected by correlating correlating drift between years i and i − 1 against the ratio of the number of isolates from year i to the number of isolates from year i−1. We found no significant correlation , suggesting our results to be unbiased with regard to sample count. We have included this analysis in the manuscript.2) Likewise, in, the negative correlation between drift in different clades in each season could, again, be driven by variation in the number of strains per clade that were subjected to HI each season. The authors could rule this out either by simulation or, more simply, be repeating their analysis after subsampling an equal number of isolates per clade each season for inclusion into the BMDS.The above analysis applies to this correlation as well.3) I found the section fitting diffusion coefficients (in different branches) to the antigenic maps confusing — if only because strict diffusion (in one or two dimensions) has no advection term and therefore no tendency to move in one direction or another; whereas the data clearly show an advective tendency (moving to the “right” over time in antigenic dimension 1). I presume the authors attempt to deal with this by imposing a prior (eq. 12) for the location of a virus based on its location in a molecular phylogeny. But this would seem to conflate antigenic drift, which the authors wish to quantify using a diffusion coefficient, with genetic mutations (many of which are driven by selection for antigenic escape). In other words, this hybrid approach of assuming a prior based on a strain's genotype, and then fitting a diffusion coefficient to summarize strength of drift, does not seem to be a pure measure of drift. What exactly the fitted diffusion coefficient means is extremely unclear to me, in any formal sense, as the procedure is so ad-hoc.x, properly separating advection and volatility in the diffusion process. The section “Antigenic evolution across influenza lineages” most reflects these changes, including the addition of a new Table 2.We agree that applying the standard measure of diffusion coefficient D based on displacement vs time, without accounting for advection was problematic. We have substantial revised the text to focus on direct parameter inference of drift parameter μ and diffusion volatility parameter σ4) How do the antigenic maps shown compare to BMDS applied to pairwise AA hamming distances alone? How many serious discrepancies that are outside the confidence intervals on map reconstruction? In other words, the authors should demonstrate that the HI data actually provide substantively different results for antigenic grouping than genetic data do alone. This was an important contention of Smith et al. (2004), but it was based on the position of only a handful of viruses in that paper. Do the authors have more convincing evidence in this study that the antigenic data are providing substantial extra information beyond the genetic data?We have revised the manuscript to include a thorough analysis of the relation- ship between genetic distance and antigenic distance. This is new [Editors’ note: the author responses to the re-review follow.]1) I like how the authors have included “virus effects” alone in the new Table 1. At least one “virus effect” could be overall receptor avidity, and Plotkin and Hensley have a recent paper  showing that avidity can influence antigenic clustering. It might be worthwhile to include a sentence on the possibility that “virus effects” could be a manifestation of avidity?We appreciate this suggestion. Including the biological explanation for why we observe virus effects in the form of decreased or increased overall HI reactivity is very important. We’ve revised this section to include references to virus avidity and relabeled ‘virus effects’ to ‘virus avidities’. This has the additional benefit of being more transparent of a term than the opaque ‘effect.’With the change from ‘virus effect’ to ‘virus avidity’ made, we chose to make a similar biological realignment of ‘serum effect’ to ‘serum potency’, i.e. some sera have higher potency than other sera, allowing them to inhibit hemagglutination at lower concentrations than other sera. The use of potency here is meant to align with the neutralizing antibody literature that distinguishes neutralization potency from neutralization breadth. Antigenic cartography has not traditionally measured breadth of hemagglutination inhibition.2) In Table 2 and the related discussion, the authors discuss the scaled effective population size Ne* τ. They never define what tau represents, and I don't think it is safe to assume that the reader will know this — I certainly don't. More interpretation here would be helpful.We’ve revised the manuscript to clarify the definition and interpretation of both Ne and τ.3) In the “Punctuated evolution and its epidemiological consequences” section, the part about subsampling to look at whether drift associates with number of isolates should be expanded. I would suggest starting a new paragraph at the current “We test to see...” sentence that briefly explains the rationale for why this test is being done . I also think that it would be nice to have a table or figure somehow representing the actual results of this analysis.We’ve expanded out this sentence to a paragraph following the paragraph on the year-to-year drift vs incidence correlation. We give rationale for the test, explain the bootstrap procedure, give p-values for both combined and separate analyses across lineages and provide a figure showing a scatterplot of the data.4)legend refers to “The mean posterior scaled effective populations... is shown for each virus.” These are not actually shown in, at least not in a way that is obvious to me.eτ had been included in Thank you for catching this. Estimates of N5) My comments where the original reason that the authors removed the argument for the across lineage correlation in incidence. However, now ‘Punctuated evolution and its epidemiological consequences’ section has this orphan paragraph beginning “Although the general correlation between rate of antigenic drift...” This paragraph doesn't seem to make sense in the context of the presented data any more, as the paper no longer has any information about across lineage correlations, so it can't even be seen what they are saying may not be causal. I think this paragraph either needs to be dramatically expended or probably better eliminated. Maybe the previous paragraph could then just get a wrap-up sentence interpreting the results to suggest a strong relationship between drift and incidence within each lineage.We agree with this advice. We have replaced this paragraph with a wrap-up sentence as suggested.6) Although the Conclusion is fine, I feel that it might benefit from a paragraph summarizing the main biological results as regards different rates of drift in lineages and the correlation between drift and incidence within lineages. These biological results are not really mentioned in the current Conclusion.We have revised the Conclusion to include more discussion of the biological results.


Seadornavirus, family Reoviridae. LNV causes lethal haemorrhage in experimentally infected mice. Twenty seven isolates of LNV were made from mosquitoes collected in different locations within the Xinjiang province of north-western China during 2005. These mosquitoes were caught in the accommodation of human patients with febrile manifestations, or in animal barns where sheep represent the main livestock species. The regions where LNV was isolated are affected by seasonal encephalitis, but are free of Japanese encephalitis (JE). Genome segment 10 (Seg-10) (encoding cell-attachment and serotype-determining protein VP10) and Seg-12  were sequenced for multiple LNV isolates. Phylogenetic analyses showed a less homogenous Seg-10 gene pool, as compared to segment 12. However, all of these isolates appear to belong to LNV type-1. These data suggest a relatively recent introduction of LNV into Xinjiang province, with substitution rates for LNV Seg-10 and Seg-12, respectively, of 2.29×10−4 and 1.57×10−4 substitutions/nt/year. These substitution rates are similar to those estimated for other dsRNA viruses. Our data indicate that the history of LNV is characterized by a lack of demographic fluctuations. However, a decline in the LNV population in the late 1980s - early 1990s, was indicated by data for both Seg-10 and Seg-12. Data also suggest a beginning of an expansion in the late 1990s as inferred from Seg-12 skyline plot.Liao ning virus (LNV) is related to Banna virus, a known human-pathogen present in south-east Asia. Both viruses belong to the genus  Seadornavirus encompasses mosquito-borne 12 segmented dsRNA viruses that have been isolated in South-east Asia. The genus is classified within family Reoviridae, subfamily SedoreovirinaeBanna virus (BAV), Kadipiro virus (KDV) and Liao ning virus (LNV) Anopheles, Culex and Aedes mosquitoes. Banna virus, which is the type species, has been isolated from mosquitoes in Indonesia (particularly in Java) ReoviridaeThe genus Banna virus was first isolated from cerebrospinal fluids (CSF) (2 isolates) and sera (25 isolates) of patients with encephalitis in the Yunnan province (Xishuang Banna prefecture) of southern China in 1987. It was also isolated in 1992 from patients with fever and flu-like manifestation Banna virus and Kadipiro virus replicate in both insect cells and in mice, although their replication in mammalian cells is restricted to BSR cells Ades albopictus C6/36 cells, which became fusiform and detached from the culture surface. RNA preparations extracted from the infected cell-cultures were analysed by 1% agarose gel electrophoresis, in each case generating a migration pattern identical to that of the prototype isolate of LNV-1 (LNV-NE9712) (data not shown).Viruses were isolated from 27 pools of mosquitoes caught during July-August 2005 in the Kashi Prefecture of Xinjiang Province in North-west China . The virAlphavirus], yellow fever virus 17D [genus Flavivirus] and Banna virus [BAV-Ch] - data not shown).RT-PCRs, using primers designed to target Seg-10 (903 bp-long) and Seg-12 (760 bp-long) of the LNV genome, generated cDNAs of the expected sizes (845 bp for Seg-10 and 675 bp for Seg-12) from all 27 virus isolates . Howeverrd base position of the codon). When compared to Seg-12 of the unique LNV-2 isolate (LNV-NE9731), nt identity levels ranged from 87.7% to 87.8%, with 99.8% to 99.9% aa identities in VP12 .Sequence analyses of Seg-10 and Seg-12 and comparisons to published data, showed that all 27 isolates belong to LNV-1 (represented by prototype isolate LNV-9712) nd position and 15.6% 1st position. Overall, the prototype LNV-1 and the new isolates showed 91.9% to 93.1% nt, and 94.2% to 96.6% aa identity, which is still consistent with membership of the same virus serotype . Of these changes 55.4% were at the 3rd position, 19.9% at the 2nd position and 24.7% at the 1st position.Comparisons of Seg-10 and VP10 from the new LNV isolates with the prototype strain of LNV-2 (LNV-NE9731), showed nt identity levels of only 75.1% to 75.3%, and aa levels of 79% to 80% - consistent with membership of different serotypes The Tajima D test of neutrality, implemented in MEGA4, was used to assess selection. The expected value for populations that conform to a standard neutral selection model is zero Previous work We carried out a further study, by injecting subcutaneously LNV-1 (isolate LNV-NE9712) into 4 mice, then re-isolating the virus from mouse blood (collected 3 days post-injection) on BSR and C6/36 cells. Cell culture passage one (P1) of the re-isolated virus was used for molecular studies. Amplicons (506 bp-long) obtained by nested PCR using primers LNV12s2 and LNV12r2  from theTwenty cDNA clones were also generated (using primers LNV10S/LNV10R) for Seg-10 of the virus re-isolated in BSR or C6/36 cells. In each case, only one codon change was identified at the same position (nucleotides 64–66: GCA→AAC), changing in alanine  into asparagine (N), compared to all field isolates of LNV-1.LNV was identified by RT-PCR in the spleen of injected mice, two weeks after injection. Sequencing showed that Seg-10 and Seg-12 of the virus present in the spleen, were identical to those of the virus re-isolated from the blood collected at 3 days post-injection, indicating that the highly divergent sequences were ‘bottlenecked’. All of the blood-derived sequences have been deposited in the Genbank database under accession numbers HM756693, HM756694 and HM745468 to HM745505.st and 2nd base variations that were detected in Seg-10 of the field isolates of LNV-1 are located between nucleotides 369 and 720, which maps to the ‘head’ of the protein monomer (aa 117 to 244), not to the ‘tail’ region where the coiled coils are located.A model for the structure of LNV-1 VP10, was generated using the programme ‘MODELLER’, based on the previously published atomic structure of the homologous BAV VP9 For phylogenetic reconstructions, we determined the shape-parameter alpha to be used for the gamma distance. This shape parameter measures the variability of the rates-of-change between different sites within a set of sequences. When alpha is >1, most sites will have similar rates of change, although when alpha is ≤1 most sites have very low rates, but with evolutionary hot spots with higher rates The shape parameter for both the nt and aa sequences of Seg-10 derived from the mosquito isolates was 0.3. The neighbour-joining trees  and 4 coA gene genealogy analysis of Seg-10 showed that 23 of the LNV-1 isolates (out of 27 isolated in Xinjiang) form a ‘network’ with a group of 5 identical isolates that :  model of site variation (GTR+Γ4). Substitution rate (expressed as substitutions/site/year) in Seg-10 was 2.29×10−4 (95%HPD: 1.29–3.99×10−4). Substitution rate in Seg-12 was 1.57×10−4 (95%HPD: 0.54×10−4–4.13×10−4). Based on these estimates, the mean time for the most recent common-ancestor for Seg-10 of LNV is 1553 years overall (95%HPD: 500.03–2919.37), with 501 (201.52–702.71) years for Seg-10 of LNV-1, and 271 (50.38–399.92) years for Seg-10 of the isolates made in 2005. In contrast, the most recent common-ancestor of segment 12, was 710 years (95%HPD: 357.50–1024.27 years), with 260 (95%HPD: 125.23–399.74 years), years for Seg-12 of LNV-1 and 91 years (95%HPD: 24.69–227.75 years), for Seg-12 of the isolates made in 2005.The best fit model for datasets of both Seg-10 and Seg-12 was found to be general time reversible (GTR), with a gamma distribution  Flavivirus) Isolations of LNV from JEV exists in an enzootic cycle between mosquitoes and pigs and/or water birds Sequence and phylogenetic analyses indicate that different sites in Seg-10/VP10 sequences of LNV-1 have low evolutionary rates, although there are also hot spots with higher rates. These analyses also point towards purifying selection and confirm that all of the isolates belong to a single serotype.Diversified sequences of segment 12 that were identified in the mouse blood relate to encapsidated genomic dsRNA. Viruses that contain these diverse sequences may not be viable and may not be able to reinitiate a productive infection in susceptible/permissive cells and would therefore be bottlenecked. The stability of the genome of this arbovirus also reflects the need to replicate in both the mosquito vector and a mammalian host.Phylogenetic trees show that LNV isolates from Boshikeranmu village and Jiashi County (approximately 50 Km apart), do not form separate clusters (at the nt or aa level) either in Seg-10 or Seg-12. Clustering within the nt tree indicates that Seg-10 of some LNV-1 isolates  is ancestral to the others. ‘Gene genealogy’ analysis also indicates that Seg-10 from LNV isolates J60, J′36 and J′41 and B7, is not part of a larger Seg-10 ‘network’, or any network among themselves. In contrast all of the Seg-12 sequences were connected in a single network, indicating that the Seg-12 gene pool (in Xinjiang province) is less heterogeneous than that of Seg-10. These analyses strongly indicate that reassortment events, involving exchange of Seg-10 and Seg-12, have occurred during the evolution of these virus strains.As seen for other arboviruses, changes in vector-host densities will also influence transmission frequency and the incidence of disease. These changes will also affect the population size of the pathogen, as it follows these fluctuations Aedes dorsalis mosquitoes in 1997, while all isolates described here were from Culex species in 2005. Banna virus, the prototype of genus Seadornavirus, was originally isolated in Indonesia in 1981, subsequently in Southern China in 1987, then 20 years later in the Northern China. The identification of LNV during 1997 The original LNV isolates were obtained from Aedes aegypti genome Aedes aegypti cell line (A20) that was cultured in 1969 from first instar larvae of the London strain of Ae. aegypti . This indicates that insertion occurred before the introduction of Ae aegypti into th and 19th century, and that LNV could have originated from Africa where the Ae aegypti originates from.Recent analyses of The haemorrhage caused by the second injection of LNV into mice was not attributed to an antibody facilitating effect Phylogenetic analyses of 40 clones of Seg-12 from mouse blood (4 mice) identified separate clusters of cDNA clones. The nucleotide sequences of mice 1, 2, 3 or 4 formed each a cluster containing sequences from a single animal. The identification of LNV in the spleen (two weeks post-injection) with identical sequences to the virus re-isolated from mouse blood, indicates that the highly divergent sequences identified directly in the blood, had been ‘bottlenecked’.Culicoides-borne orbivirus), were previously found to be within a similar order of magnitude and ranged from 0.5×10−4 to 7×10−4 substitutions/nt/year Phylogenetic analyses, genealogy, animal and cell culture work on the field samples show that LNV Seg-12 varies less than Seg-10. This mirrors findings concerning the variability of genome segments encoding cell-attachment outer-capsid proteins, of other dsRNA arboviruses, particularly the orbiviruses Banna virus is implicated as a cause of disease in humans An ELISA, based on the serotype-specific protein VP10 of the two known serotypes of LNV, has been developed and is being used for epidemiological surveys of LNV in China, particularly in Xinjiang. Real time RT-PCR systems based on Seg-10 and Seg-12 of LNV have also been developed for the same purpose. These studies include humans, domesticated and wild animals  and will help clarify if LNV is the causative agent of disease outbreaks in China, and help to identify potential risks that may be involved. We will also attempt to identify the epidemiological impact of LNV in neighboring Asian countries.No specific permits were required for the described field studies. The work was done under the general supervision of the Chinese centers for disease control and prevention. No specific permission was required for trapping of mosquitoes. The trapping locations are not protected in any way. The field studies did not involve endangered or protected species.The ethics review committee of the Institute has reviewed and approved the proposed experimental infection of LNV in mice.Mosquitoes were collected during July–August of 2005, using light traps, in human accommodations of patients with febrile manifestations, and in animal barns where sheep represent the main livestock, in Xinjiang province of China. Collections were made in Jiashi County and in Boshikeranmu village, situated in the Kashi prefecture of Xinjiang province in the north-west of China. The village and county are both located ∼50 Km from Kashi city. The collected mosquitoes were frozen for 30 min for −20°C, then sorted on an ice plate.Blood-fed and male mosquitoes were excluded. Eighty to two hundred mosquitoes were sorted into each collecting tube and stored in liquid nitrogen.Aedes albopictus C6/36 Aedes albopictus C6/36 cells at 28°C in MEM for further agarose gel electropherotyping, PCR amplifications and sequencing. Virus isolates were checked for their relatedness to Japanese encephalitis virus, Banna virus and Liao ning virus, by RT-PCR, using the primers shown in For virus isolation, 2 ml of minimal essential medium , supplemented with 2% foetal bovine serum, 100 U penicillin/ml and 100 U streptomycin/ml, was added to each mosquito pool, followed by homogenisation in pre-cooled sterile plastic ‘grinding’ tubes. The ground samples were centrifuged at 12,000 g for 20 min at 4°C, and the clarified supernatant was assayed for virus by inoculation onto Aedes dorsalis mosquitoes, in the Liaoning province in northwest China in 1997) of serotype 1 was injected subcutaneously into mice. Viremic blood was collected at 3 days post-injection, before antibodies had been produced (absence of anti-LNV IgMs or IgGs: data not shown). LNV was re-isolated from mouse blood on BSR or C6/36 cells.The prototype strain of LNV serotype 1  infected by the virus from mouse blood using Trizol. RNA extracts were converted to cDNA as described above.Specific primers were designed from sequences of genome segment 12 (Seg-12) of LNV . These wAmplicons derived from the blood and cell culture re-isolated virus were ligated into the pGEM-T vector (Promega) and transformed into bacteria. Forty plasmid clones from mouse blood (10 from each mouse) were sequenced using M13 primers. Another 80 clones from cell cultures were also sequenced (20 clones each for Seg-12 and Seg-10 from BSR cells and another 20 clones each for Seg-12 and Seg-10 from C6/36 cells).Nucleotide (nt) and amino acid (aa) sequence alignments were generated using ClustalX version 1.8 For gamma distance calculations (Kimura-2 parameter and Poisson correction models), the shape parameter alpha was determined using the PAML package Gene genealogy analysis was carried out using the programme TCS The best fit model of nucleotide substitution to be used in Bayesian coalescent analyses was determined using jModelTest (v 0.1.1) Bayesian coalescent analysis based on Markov Chain Monte Carlo (MCMC) sampling Models for the outer capsid protein of LNV were generated using the Programme MODELLER


Humans in contact with macaques risk exposure to enzootic primateborne viruses. Cercopithecine herpesvirus 1 (CHV-1), simian virus 40 (SV40), simian retrovirus (SRV), simian T-cell lymphotropic virus (STLV), simian immunodeficiency virus (SIV), and simian foamy virus (SFV). Antibody seroprevalence was 94.9% to RhCMV (37/39), 89.7% to SV40 (35/39), 64.1% to CHV-1 (25/39), and 97.4% to SFV (38/39). Humans who come into contact with macaques at Swoyambhu risk exposure to enzootic primateborne viruses. We discuss implications for public health and primate management strategies that would reduce contact between humans and primates.The threat of zoonotic transmission of infectious agents at monkey temples highlights the necessity of investigating the prevalence of enzootic infectious agents in these primate populations. Biological samples were collected from 39 rhesus macaques at the Swoyambhu Temple and tested by enzyme-linked immunosorbent assay, Western blot, polymerase chain reaction, or combination of these tests for evidence of infection with rhesus cytomegalovirus (RhCMV),  Macaca mulatta) that lives in close proximity to humans.Most pathogens that affect humans are thought to have originated in animals and subsequently evolved to successfully parasitize human populations , also known as herpes B virus, is a member of the taxonomic subfamily Alphaherpesviridae. Serologic evidence of infection with CHV-1 has been documented in several species of macaques  is a polyomavirus enzootic among some species of Asian macaques, including rhesus macaques of northern India and Nepal. SV40 is present in the genitourinary tract of infected macaques and is thought to be transmitted through ingestion of urine containing the virus , ,,–Macaques harbor several enzootic retroviruses, including simian foamy virus (SFV), simian type D retrovirus (SRV), and simian T-cell lymphotropic virus (STLV). SRV and SFV are present in saliva and other body fluids of infected macaques, which suggests that bites, scratches, and mucosal splashes with macaque body fluids can transmit infection (STLV is closely related to human T-cell lymphotropic virus (HTLV-1). Asymptomatic infection with STLV-1 is common among primate hosts. STLV is hypothesized to be the progenitor of HTLV through multiple cross-species transmissions (Simian immunodeficiency virus (SIV) is widely distributed among African primates and has been shown to infect humans who come into contact with them  were trapped, sampled, and released. Samples were obtained as part of a comprehensive health screening effort conducted at the request of the Federation of Swoyambhu Management and Conservation Committee. Macaques were trapped in a portable cage measuring 2.5 × 2.5 × 1.5 m and sedated with 3 mg/kg intramuscular tiletamine HCl/zolazepam HCl. To avoid stressing young animals, infants were not anaesthetized or sampled as part of this protocol. All anesthetized macaques were given a complete physical examination, and using universal precautions and sterile technique, we collected 10 mL blood by venipuncture of the femoral vein; 8 mL blood was centrifuged to extract serum. The remaining blood was aliquotted into Vacutainer vials containing EDTA. Unique study identification numbers were assigned to all specimens collected from each animal. Serum and whole blood were frozen in the field, then stored at –70°C. Animals were tattooed on their inner right thigh for identification and future follow-up. Each macaque's weight and dental formula were collected and recorded for age assessment. Age was estimated on the basis of observed dental eruption sequence. After sample collection, animals were placed in a cage and allowed to recover fully from anesthesia before being released as a group back into their home range. This data collection protocol was reviewed and approved by the University of Washington Institutional Animal Care and Use Committee (#3143-03).,,After necessary national and international permits were obtained, the samples were shipped to the United States, where they were analyzed at several institutions. Enzyme immunoassays for anticapsid antibodies to SV40 were performed as described previously . Statistical associations between macaque viral seropositivity, sex, age class, and group number were determined by χ2 to test associations by sex, group number, and age category. The results from these tests show no significant association between seropositivity for antibodies to RhCMV, SV40, or SFV and age, sex, or group number of the macaque. However, a significant (χ2 p<0.0001) age-related effect was seen for CHV-1. Seroprevalence of antibodies to CHV-1 increased from 23.1% (3/13) among juveniles to 100% (19/19) among adult macaques.The results of the serologic and PCR assays were analyzed by using χM. mulatta) from India . Increased population densities characteristic of captive settings may facilitate viral transmission, providing a possible explanation for this observation.The absence of SIV in the Swoyambhu macaque population is unsurprising, given that SIV has yet to be detected in natural populations of Asian primates. This finding, however, does not eliminate the possibility that the situation could change in the future. While SIV is typically found only among African primates, this virus could be introduced into Asian primate populations through the global market trade in animals, in which pet primates can be purchased (,,–,,,,,A growing literature suggests that cross-species transmission of infectious agents occurs between humans and several primate species in a variety of contexts and in diverse areas (The dynamics of human-macaque contact at Asian monkey temples differ substantially from those in laboratories and zoos, which may make cross-species transmission more likely at monkey temples. Because primate laboratories have been promoting specific pathogen-free colonies, the risk for primate-to-human transmission of enzootic agents in these settings is likely to diminish over time. Additionally, primate laboratories require the routine use of eye protection, gloves, and protective garments. Injury protocols call for thorough irrigation of wounds and close follow-up of exposed persons. In contrast, research examining wound care practices among exposed workers at the Sangeh monkey forest in Bali (From a global infection control standpoint, learning about primate-to-human transmission at monkey temples like Swoyambhu is particularly relevant. Because the number of humans who come into contact with primates at monkey temples around the world is probably several million per year (In spite of centuries of human-primate commensalism in Asia, human disease has yet to be causally linked to enzootic primateborne viruses. However, disease may go undetected because of low incidence, inadequate surveillance, and lack of awareness. Latency between infection and disease manifestation could mask the association between primate exposure and disease. Though long-term commensalism may lead to increased immunity to primateborne pathogens among exposed human populations, non-Asian visitors might be vulnerable, and increased travel to Asia would expose more nonimmune persons to primateborne pathogens. Finally, as HIV continues to spread in Asia, increasing numbers of immunosuppressed persons will likely be exposed to primateborne infectious agents. Immunocompromised hosts could provide pathogens a "permissive" environment in which to evolve into more pathogenic forms.In taking steps to reduce the risk for cross-species transmission between humans and primates, we should first understand how transmission occurs, i.e., the situations, conditions, and behavior that lead to contact and transmission. Because feeding macaques is thought to account for most interactions between humans and macaques, adopting protocols to restrict feeding to persons specifically trained for that purpose could reduce the number of visitors who come into direct contact with macaque body fluids. Educating visitors as well as persons who live near monkey temples to avoid behavior that leads to bites and scratches could reduce risk. Finally, availability and awareness of proper protocol for effective wound irrigation has the potential to reduce transmission of infection. No data on the efficacy of postexposure prophylaxis with antiviral agents are available, but pharmacologic therapy is another tool that could reduce the likelihood of cross-species viral transmission. In addition, monitoring human populations for infection with primate viruses at the human-primate interface is a prudent strategy to facilitate the early detection of primateborne zoonoses.This information must be put into context. The recent culling of macaques at a wildlife park in England and at monkey temples in Hong Kong and Taiwan in response to the perceived threat of zoonotic transmission of CHV-1 is an example of an exaggerated response to an inadequately understood risk. Improving awareness of zoonotic transmission and effective management strategies among the public as well as among persons who manage primate parks and temples will be instrumental in allowing humans and primates to continue to coexist.


Vaccinium spp. or other native plants from cultivated fields. The aim of this paper is to highlight the importance of blueberry and cranberry viruses, focusing not only on those that are new but also those that are emerging as serious threats for production in North America and around the world. Blueberry and cranberry are fruit crops native to North America and they are well known for containing bioactive compounds that can benefit human health. Cultivation is expanding within North America and other parts of the world raising concern regarding distribution of existing viruses as well as the appearance of new viruses. Many of the known viruses of these crops are latent or asymptomatic in at least some cultivars. Diagnosis and detection procedures are often non-existent or unreliable. Whereas new viruses can move into cultivated fields from the wild, there is also the threat that devastating viruses can move into native stands of  Vaccinium belongs to the Ericaceae (heath family). The family has more than 3,500 species growing in all latitudes, from the tropics to the polar regions. A common feature among members of the family is that they grow in acidic, many times, nutrient-deprived soils [Vaccinium, highbush, lowbush and rabbiteye blueberries  and cranberry (V. macrocarpon Ait.) are of high economic importance. Largely due to recent press regarding the health benefits of fresh fruit consumption [The genus ed soils . Of the sumption ,3,4, incsumption . BlueberVaccinium spp. are known to harbor viruses belonging to several different families. Virus diseases are accompanied by a variety of symptoms ranging from being visually asymptomatic to plant death. In addition, symptoms can vary between cultivars, regions, production systems and years. Viruses can also be latent for years or not express at all in a nursery setting, further allowing widespread distribution. Virus diseases, particularly of blueberry, have become a major problem in recent years not only because of the expansion of the industry and the change in climate patterns, but also because of lack of grower awareness of the problems that can emerge when propagating non-certified material. Because of the rapid increase in blueberry plantings in recent years, in many cases growers choose to propagate from field plants that could be harboring asymptomatic infections at the time cuttings were taken. This has led to the emergence of several diseases in new areas or to major increases in virus incidence where there was minimal virus presence [Like most plants, presence .This review not only aims to provide up-to-date knowledge of the viruses that infect blueberry and cranberry but also give insight into the measures that need to be taken to control the spread of virus diseases and avoid virus epidemics, so as to be able to grow these crops in a sustainable and profitable environment. As is the case with virus diseases of any vegetatively propagated crop, the most critical control measure is the production and planting of stocks free of targeted pathogens. In the U.S., the programs for producing certified planting stocks of vegetative fruit crops has received a tremendous boost with the development of the National Clean Plant Network (NCPN). NCPN has as its’ mission the production and maintenance of the initial starting materials for certification programs that are free of targeted pathogens. Thus, the program aims to introduce cultivars of plants that are needed or desired for fruit production, including new materials developed outside of the U.S. to protect current production and the environment from the introduction of potentially damaging pathogens. The structure of this review is primarily based on the mode of transmission of the viruses as this is the most important factor for the development of an efficient and effective management strategy for the viruses and the diseases they cause.Chenopodium quinoa and Nicotiana benthamiana it appears to be asymptomatic in the nine highbush cultivars evaluated [Peach rosette mosaic virus, a subgroup C Nepovirus that also infects blueberry. The genome organization of BLSV is typical of members of subgroup C, with the replication-associated polyprotein in RNA 1, and the movement and coat proteins of the virus encoded in the single polyprotein of RNA2. The only difference in BLSV, as compared to other subgroup C viruses is that it has a serine instead of a cysteine protease encoded in RNA 1 [Blueberry latent spherical virus (BLSV) was isolated recently from highbush blueberry in northern Japan . Althougvaluated . BLSV isin RNA 1 . Given tBlueberry leaf mottle virus (BLMoV). The physicochemical properties of the virus and the weak cross-reactivity of Grapevine Bulgarian latent virus (GBLV) to BLMoV antisera pointed to the relationship of the virus to members of the genus Nepovirus [. Partial sequence of the two RNAs of the virus placed BLMoV in subgroup C of the genus [Nepovirus, nematode or aphid transmissions studies have proven unsuccessful. The virus is readily transmissible by pollen, primarily via honeybee movement and seed [Cherry leaf roll virus [Blueberry leaf mottle disease was first described in 1977 in Michigan and its presence is limited to this state and parts of eastern Canada . Affecteepovirus . Partialhe genus ,15. Althand seed  similar ll virus . The twoPeach rosette mosaic virus (PRMV), early on was only found in stone fruit trees, grapevine and a few weed species such as dandelion. It was first reported in highbush blueberry in 1981 when northern highbush blueberry was planted into fields previously occupied by PRMV-diseased grapevines [Nepovirus [Xiphinema and Longidorus [Rosette mosaic disease of peach was first described in the early 1900s . It tookapevines . Symptomepovirus ,21. PRMVngidorus ,23. The ngidorus ,25. For Xiphinema americanum, and disease symptoms in the field often appear as oval foci typical for nematode transmitted diseases. Both viruses cause a slow, steady decline in bush productivity in susceptible cultivars, including ‘Collins’, ‘Elliott’, ‘Jersey’, ‘Pemberton’, ‘Rubel’, and in several halfhigh blueberry clones in New Brunswick [Necrotic ringspot disease was first reported in blueberry in 1960 in New Jersey  and was runswick . NecrotiNepovirus [Rubus spp. [TRSV and ToRSV are typical of members in subgroups A and C, respectively, in the genus epovirus . They cobus spp.  in the sX. americanum and the viruses prior to planting, since the viruses and nematode have broad host ranges and are symptomless in many hosts. TRSV and ToRSV are often difficult to detect in blueberry due to uneven distribution in the plants , seasonaBlueberry scorch virus [Blueberry shock disease was first observed in Washington in 1987 and initially confused with blueberry scorch caused by ch virus . Howeverch virus . Howeverch virus . The yeach virus  and Michch virus . Ilarvirus. It is detected readily by ELISA or RT-PCR from flower buds early in the season and leaf tissue through August in the PNW. Control should be focused on not introducing the virus to new production areas on nursery stock. Once the virus is present in a field, removal of infected plants based on symptoms or diagnostics will slow the spread of the virus but not completely prevent further spread. There are several reasons for this: (1) The virus is unevenly distributed in blueberries the first year or two after infection and field testing can easily result in false negatives; and (2) The virus is pollen-borne and the flowers open enough to start releasing pollen before symptoms appear, thus transmission occurs before it is obvious a plant is infected. The recommendation in the PNW is to let the virus run its course through a field realizing that there will be a 1–2 year crop loss in the process. This is less of a loss than removal of a field and replanting, where it may be 4–6 years before a planting is back in full production. It is unknown whether plants will recover in a similar manner in other production areas where the growing conditions are less favorable. Thus, an approach similar to that used in Michigan, where the entire field was removed as soon as the virus was detected, is recommended if the virus is detected in a new production area.BlShV is a member of subgroup 3 of the genus Tobacco streak virus (TSV) was first reported in cranberry in 2001 [Ilarvirus genus in the family Bromoviridae. Although there are both immunological and molecular tests available for the detection of the virus, the extreme diversity observed among isolates needs to be taken into consideration when choosing a detection protocol. TSV has been reported to be thrips transmitted through incidental mechanical inoculation as the insects feed on the plants and infected pollen is introduced into the wounds [ in 2001 . The vir in 2001  and the e wounds . This moe wounds . Blueberry scorch virus (BlScV). The virus particles are flexuous rods approximately 14 × 650 nm encapsidating the monopartite positive-sense single-stranded RNA genome of about 8.5 Kb [A previously undescribed blight of highbush blueberry was observed since the early 1970s in the Sheep Pen Hill area of Burlington County, New Jersey . The symt 8.5 Kb ,48. V. macrocarpon) and black huckleberry (V. membranaceum), but those infections were asymptomatic [Chenopodium quinoa and C. amaranticolor [N. occidentalis [Symptoms vary depending on virus strain and cultivar from asymptomatic to severe blighting of flowers, young leaves and twig dieback. In addition to these symptoms, leaves of infected bushes sometimes show marginal chlorosis or a red line pattern in late summer and fall . The bliptomatic ,54. An anticolor  as well dentalis . Mechanidentalis . Blueberdentalis . It is iThe disease was first described in 1950 . ExpandiBlueberry shoestring virus (BSSV), was proven to be the causal agent of the disease after Lesney et al. [Sobemovirus and it is recognized as a member of that genus by the International Committee on Taxonomy of Viruses [Sobemovirus genus that is transmitted by an aphid in an efficient manner that can reach 28% in inoculation studies with 30 aphids (Illinoia pepperi) that had an inoculation access period of one hour [Varney  providedy et al.  purifiedy et al.  studied  Viruses . BSSV isone hour . All typone hour . As in sA new disorder was observed in 2002 in Oregon and Washington in the U.S. and British Columbia, Canada. Highbush blueberry set but dropped virtually all fruit when about 5 mm in diameter. The possibility that a virus was associated with the disorder was examined and dsRNA was isolated from more than 20 affected and several asymptomatic plants. A single molecule of about 3.5 kb was present in the majority of affected plants and controls . The molV. corymbosum) cultivars develop symptoms [Vaccinium species other than V. vacillans  or native rabbiteye blueberries (V. virgatum) in the region. The symptoms initially appear as distinct necrotic rings with green centers . CiLV has two RNAs compared with the four present in BNRBV. It appears that RNA 1 of CiLV is divided into RNA 1 and RNA 2 of BNRBV whereas RNA 2 of CiLV is divided into RNA 3 and RNA 4 of BNRBV. Thus, BNRBV likely represents a new virus genus [Hibiscus green spot virus also shows similarities to BNRBV but has three genomic segments and triple gene block type of movement proteins in contrast to the 30K-superfamily type of CiLV and BNRBV.Investigations into the possibility of a viral etiology began with extraction of dsRNA from symptomatic and healthy leaf tissues. Six dsRNA bands were observed consistently from more than 10 sources of diseased tissue B, four ous genus . HibiscuThe virus is detected readily by RT-PCR as there was a perfect correlation between symptomatic and asymptomatic tissue and RT-PCR positive and negative results, respectively, in over 60 isolates tested from multiple states. Future plans include testing that will address symptomless infections in rabbiteye, northern highbush, and additional cultivars of southern highbush blueberries.Based on aa homologies with CiLV it is likely that BNRBV is transmitted by an eriophyid mite, and experiments to test this hypothesis are underway in Florida and Georgia ,78. In gi.e., ‘Star’, ‘O’Neal’, and ‘FL 86-19’), whereas others on the same farm or field can be symptomless. Although spread through vegetative propagation appears to be limited, suspect plants should not be utilized for propagation through hard- or softwood cuttings. Plants produced in tissue culture from source plants that have tested free of BNRBV may help to reduce the initial introduction of this disease since they are protected from transmission by an aerial vector during much of the plant increase propagation cycles. There is strong evidence for host resistance or tolerance, as some southern highbush blueberry cultivars are often found to exhibit extensive symptoms (Caulimoviridae in the Soymovirus genus and designated Blueberry red ringspot virus (BRRV). Virus particles are 42–46 nm in diameter and the circular double-stranded DNA genome is 8.3 Kb [Soymovirus, are yet to be identified, although the virus is actively moving in infected fields in the eastern U.S. The disease has been reported in blueberry in New Jersey, Michigan, North Carolina, Georgia, New York, and Connecticut, [Red ringspot was first described as a disease of highbush blueberry in New Jersey in 1950 . The diss 8.3 Kb . The virs 8.3 Kb . Vectorsecticut, . The disecticut, , Korea [ecticut, , Poland ecticut, , Sloveniecticut, , and Czeecticut, . Since secticut, . Closterovirus was present in affected plants. Detection primers were developed, yielding amplicons of the new virus only from symptomatic plants [A new disorder named blueberry bronze leaf curl was observed in 2009 in Michigan in the U.S. Symptoms were observed in cultivars ‘Jersey’, ‘Bluecrop’, ‘Elliott’, ‘Duke’ and ‘Pemberton’ . Plants c plants . A clostc plants . This suBlueberry red ringspot virus appears to spread much more rapidly there than in other regions where it has been reported, suggesting the presence of a more efficient vector. It has been more than 15 years since the last review of blueberry viruses . In thisetc. leading to changes in virus distribution. With the large scale of plant  movement internationally, introduction of new vectors into a production region can dramatically change the epidemiology of a virus disease . Once a virus is introduced into a new region eradication efforts can be very expensive (i.e., Plum pox virus eradication efforts after its introduction in Pennsylvania has cost the state more than 53 million dollars [Changes in cultural practices, changes in climate and reduced or altered pesticide use may all lead to increased virus incidence through indirect impacts on vector populations. As new cultivars are released, they may be more sensitive to virus infections and exhibit more severe symptoms and reduced yields. Alternatively, they may be tolerant of virus infection and facilitate movement of viruses in plants simply because they are symptomless carriers of a virus. Changes in climate likely will result in altered ranges of many virus vectors, such as, whiteflies, aphids, thrips, mealybugs, eriophyid mites  dollars .Blueberries are an expensive crop to establish, requiring extensive site preparation, irrigation, pruning and 3–5 years to bring into production. The cost of certified plants is relatively small compared to all of the other costs associated with getting a planting into production, yet using virus infected plants could lead to a completely unproductive planting, or at best a planting that takes longer to get into production and will have reduce yields over the life of the planting. Given the importance of the quality of mother plants and certification schemes that will minimize the movement of infected material, our group is working to provide material of the highest quality possible to the end user. Through the National Clean Plant Network we are working to eliminate all known viruses from propagation material, while developing sensitive and reliable tests for all blueberry viruses based on several isolates collected from multiple states in the U.S. and countries around the world. In addition, we study emerging diseases attributed to graft transmissible agents. This work aims to identify the agents and develop detection protocols that would allow for early detection and minimize movement within the indigenous area but also reduce the possibility of movement into areas where diseases are absent. As a systems-based approach to blueberry virus diseases we are also developing harmonized certification schemes among all blueberry production states in the U.S. that will allow for the movement of the highest quality propagation material to all areas where blueberry is grown.