Keyao Pan

The epitope regions of H1-subtype influenza A, with application to vaccine efficacy

The recent emergence of H1N1 (swine flu) illustrates the ability of the influenza virus to create antigens new to the human immune system, even within a given hemagglutinin and neuraminidase subtype. This new H1N1 strain is sufficiently distinct, for example, from the A/Brisbane/59/2007 (H1N1)-like virus strain of influenza in the 2008/09 Northern hemisphere vaccine that protection is not expected to be substantial. The human immune system responds primarily to the five epitope regions of the hemagglutinin protein. By determining the fraction of amino acids that differ between a vaccine strain and a viral challenge strain in the dominant epitope regions, a measure of antigenic distance that correlates with epidemiological studies of H3 influenza A vaccine efficacy in humans with R2 = 0.81 is derived. This measure of antigenic distance is called pepitope. The relation between vaccine efficacy and pepitope is given by E = 0.47 – 2.47 × pepitope. We here identify the epitope regions of H1 hemagglutinin, so that vaccine efficacy may be reliably estimated for H1N1 influenza A.

Quantifying selection and diversity in viruses by entropy methods, with application to the haemagglutinin of H3N2 influenza

Many viruses evolve rapidly. For example, haemagglutinin (HA) of the H3N2 influenza A virus evolves to escape antibody binding. This evolution of the H3N2 virus means that people who have previously been exposed to an influenza strain may be infected by a newly emerged virus. In this paper, we use Shannon entropy and relative entropy to measure the diversity and selection pressure by an antibody in each amino acid site of H3 HA between the 1992–1993 season and the 2009–2010 season. Shannon entropy and relative entropy are two independent state variables that we use to characterize H3N2 evolution. The entropy method estimates future H3N2 evolution and migration using currently available H3 HA sequences. First, we show that the rate of evolution increases with the virus diversity in the current season. The Shannon entropy of the sequence in the current season predicts relative entropy between sequences in the current season and those in the next season. Second, a global migration pattern of H3N2 is assembled by comparing the relative entropy flows of sequences sampled in China, Japan, the USA and Europe. We verify this entropy method by describing two aspects of historical H3N2 evolution. First, we identify 54 amino acid sites in HA that have evolved in the past to evade the immune system. Second, the entropy method shows that epitopes A and B on the top of HA evolve most vigorously to escape antibody binding. Our work provides a novel entropy-based method to predict and quantify future H3N2 evolution and to describe the evolutionary history of H3N2.

A novel sequence-based antigenic distance measure for H1N1, with application to vaccine effectiveness and the selection of vaccine strains

H1N1 influenza causes substantial seasonal illness and was the subtype of the 2009 influenza pandemic. Precise measures of antigenic distance between the vaccine and circulating virus strains help researchers design influenza vaccines with high vaccine effectiveness. We here introduce a sequence-based method to predict vaccine effectiveness in humans. Historical epidemiological data show that this sequence-based method is as predictive of vaccine effectiveness as hemagglutination inhibition assay data from ferret animal model studies. Interestingly, the expected vaccine effectiveness is greater against H1N1 than H3N2, suggesting a stronger immune response against H1N1 than H3N2. The evolution rate of hemagglutinin in H1N1 is also shown to be greater than that in H3N2, presumably due to greater immune selection pressure.

Understanding Original Antigenic Sin in Influenza with a Dynamical System

Original antigenic sin is the phenomenon in which prior exposure to an antigen leads to a subsequent suboptimal immune response to a related antigen. Immune memory normally allows for an improved and rapid response to antigens previously seen and is the mechanism by which vaccination works. I here develop a dynamical system model of the mechanism of original antigenic sin in influenza, clarifying and explaining the detailed spin-glass treatment of original antigenic sin. The dynamical system describes the viral load, the quantities of healthy and infected epithelial cells, the concentrations of naïve and memory antibodies, and the affinities of naïve and memory antibodies. I give explicit correspondences between the microscopic variables of the spin-glass model and those of the present dynamical system model. The dynamical system model reproduces the phenomenon of original antigenic sin and describes how a competition between different types of B cells compromises the overall effect of immune response. I illustrate the competition between the naïve and the memory antibodies as a function of the antigenic distance between the initial and subsequent antigens. The suboptimal immune response caused by original antigenic sin is observed when the host is exposed to an antigen which has intermediate antigenic distance to a second antigen previously recognized by the hosts immune system.

Selective Pressure to Increase Charge in Immunodominant Epitopes of the H3 Hemagglutinin Influenza Protein

The evolutionary speed and the consequent immune escape of H3N2 influenza A virus make it an interesting evolutionary system. Charged amino acid residues are often significant contributors to the free energy of binding for protein–protein interactions, including antibody–antigen binding and ligand–receptor binding. We used Markov chain theory and maximum likelihood estimation to model the evolution of the number of charged amino acids on the dominant epitope in the hemagglutinin protein of circulating H3N2 virus strains. The number of charged amino acids increased in the dominant epitope B of the H3N2 virus since introduction in humans in 1968. When epitope A became dominant in 1989, the number of charged amino acids increased in epitope A and decreased in epitope B. Interestingly, the number of charged residues in the dominant epitope of the dominant circulating strain is never fewer than that in the vaccine strain. We propose these results indicate selective pressure for charged amino acids that increase the affinity of the virus epitope for water and decrease the affinity for host antibodies. The standard PAM model of generic protein evolution is unable to capture these trends. The reduced alphabet Markov model (RAMM) model we introduce captures the increased selective pressure for charged amino acids in the dominant epitope of hemagglutinin of H3N2 influenza (R2xa0>xa00.98 between 1968 and 1988). The RAMM model calibrated to historical H3N2 influenza virus evolution in humans fit well to the H3N2/Wyoming virus evolution data from Guinea pig animal model studies.

Comment on Ndifon et al., "On the use of hemagglutination-inhibition for influenza surveillance: Surveillance data are predictive of influenza vaccine effectiveness".

In 2006, Gupta et al. published an analysis of vaccine efficacy in humans for H3N2 influenza A. We collected vaccine efficacy data from the epidemiological literature for years between 1971 and 2003. In total, 19 efficacy values were obtained. We determined correlations between vaccine efficacy and four measures of antigenic distance. The first measure is the fraction of amino acid differences between the vaccine strain and the dominant circulating strain in the hemagglutinin HA1 sequence, psequence. The second measure is the fraction of amino acid differences between the dominant epitope of the vaccine and dominant circulating strain, pepitope. The third measure is the logarithm base 2 of the ratio of homologous to heterologous titers, d1 [3]. The fourth measure is the square root of the ratio of the homologous titers to the heterologous titers, d2 [4]. The correlations of the different measures of antigenic distance with vaccine efficacy are shown in Table 1. n n n nTable 1 n nCorrelation of H3N2 Influenza A vaccine efficacy in humans with different measures of antigenic distance n n n nIn 2009, Ndifon et al. published an analysis of a subset of these data, 11 data points, with some modifications [2]. Four new data points were added: some early data from 1968 and 1969, data for 1980/1981, and recent data for 2004/2005. Data for 1971/1972, provided in [1], were omitted from [2]. n nThere are a number of discrepancies in the data of Table 1 of Ndifon et al. [2]. In 1972/1973 the vaccine strain was listed as A/Hong Kong/1/68. The correct vaccine strain was A/Aichi/2/68 (also known as X31) [1,5]. A value of pepitope of 0.263 was listed in [2], rather than the correct 0.190 [1]. In 1994/1995, a dominant circulating strain of A/Shangdong/9/93 was listed [2], rather than a mixture of strains that more closely resemble A/Johannesburg/33/94 than A/Shangdong/9/93 [1,6], and which we represent by the former [1]. A value of pepitope of 0 was listed in [2], rather than 0.105 [1]. In 1995/1996 the vaccine efficacy was listed as 42.0% [2], rather than 45% [1]. In 1996, vaccine and circulating strains of A/Wuhan/359/95 and A/Nanchang/933/95 were listed [2] instead of the correct A/Nanchang/933/95 vaccine and A/Wuhan/359/95 US CDC-determined circulating strain [1,7,8]. In 1997, the dominant circulating strain of A/Nanchang/933/95 was listed [2] instead of A/Wuhan/359/95 [1,8], leading to pepitope of 0 [2] instead of 0.095 [1]. In 1997/1998 a pepitope value of 0.227 [2] was listed instead of the correct 0.238 [1](an inconsistent definition of pepitope was used in [2]). The vaccine discrepancies in [2] stem from the incorrect assumption that the WHO “recommended” strain was administered, rather than the “like” vaccine strain that was actually manufactured and administered [5–8]. Finally, in 2003/2004 efficacy data for individuals vaccinated within 2 weeks of illness were removed from the dataset. Removing these 9 individuals from the data of Ref. 35 of [1], decreases the vaccine efficacy from 12% to 0.7%. This change is within the noise of the data, and makes little difference to the results (see below). n nOnce these amendments are made, the 23 data in aggregate from [1,2] reveal a correlation between vaccine efficacy and the pepitope theory of R2 = 0.76 (see Figure 1). We focus here on the difference between pepitope and the rAHM measure of antigenic distance reported as correlating well with vaccine efficacy in [2]. We note that the definition of rAHM is identical to that of d2. In [2], only half of the data were used, those for which the vaccine and dominant circulating strain were distinct, a total of 11 data points. These 11 data points were used to test the pepitope, psequence, and d2 = rAHM measures of antigenic distance. With the corrections discussed above made, there are 14 data points fitting this criterion. If a large amount of vaccine efficacy data were available, removing a small subset of data would not be problematic. Removing 50% of the data, so that there are no data for small to moderate antigenic distances, led to a number of artifacts in [2]. The first artifact is that the linear fit of rAHM to the 11 data points of vaccine efficacy extrapolates to a vaccine efficacy of 18% when the vaccine is identical to the dominant circulating strain, with R2 = 0.56 (see Figure 1, left insert). While the R2 is sizable, the prediction of 18% is discrepant from the average vaccine efficacy of 43% [1,2] when the vaccine is identical to the dominant circulating strain. That rAHM does not predict moderate antigenic distances well is made clear when the rAHM data are fit to all years, with R2 = 0.54 instead of R2 = 0.76 for the pepitope theory. In [2] was also reported that the correlation coefficient of the standard d1 measure of antigenic distance used by vaccine designers with vaccine efficacy is R2 = 0.01. When pepitope is fit to the amended 14 data points, the linear fit extrapolates to a vaccine efficacy of 27% for an identical vaccine and dominant circulating strain (see Figure 1, right insert), with R2 = 0.27, and not almost zero as reported in [2]. The prediction of the pepitope theory is more accurate than that of the rAHM measure on these out-of-sample data, although the correlation coefficient is lower. We note that 4 out of the 6 points with pepitope > 0.19 have a negative efficacy. This predictive ability is rather similar to that of the rAHM data, for which 4 out of the 5 points with rAHM > 5 have negative vaccine efficacy [2]. In the 2004/2005 season, both A/California/7/2004 and A/Fujian/411/2002 were circulating strains, in addition to a substantial amount of circulating influenza B. The antigenic distance between A/Wyoming/3/2003 and A/California/7/2004 is pepitope = 0.286, and the antigenic distance between A/Wyoming/3/2003 and A/Fujian/411/2002 is pepitope = 0.095. Thus, while the predicted efficacy for the former is not positive, for the latter it is 20%. Antigenic distance for A/California/7/2004 alone cannot predict the expected vaccine efficacy against multiple nearly-dominant circulating strains. Indeed, the reported efficacy of 9.2% [2] is roughly the average of the 0% and 20% predicted efficacies from the pepitope theory. When the 2004/2005 data point is eliminated, the pepitope prediction extrapolates to 37% efficacy for identical vaccine and dominant circulating strain, with R2 = 0.46. If the efficacy of the 2003/2004 data point is changed from 12% to 0.7%, there is little change to this result: R2 = 0.44 with an extrapolation of 37% efficacy for pepitope = 0. n n n nFigure 1 n nVaccine efficacy versus the pepitope or rAHM measures of antigenic distance. In inset are the data for which the vaccine and dominant circulating strain are distinct. n n n nIn summary, the pepitope theory is more accurate and has a larger R2 value than the rAHM ferret animal model data when all the human H3N2 influenza A vaccine efficacy data are considered. When trained on half of the data, the pepitope theory more accurately predicts the out-of-sample, small and moderate antigenic distance efficacies than does the rAHM data. When the data point for the 2004/2005 season with multiple nearly-dominant circulating strains is removed, the pepitope theory and rAHM data fit have similar R2 values on years for which the vaccine and dominant circulating strains are distinct. Both pepitopeand rAHM predict that vaccine efficacy decreases to zero beyond a critical antigenic distance, given by pepitope* = 0.19. We note that the pepitope theory requires only sequence information, whereas rAHM is constructed from hemagglutinin inhibition data measured in ferrets.

Evolution of H3N2 Influenza Virus in a Guinea Pig Model

Studies of influenza virus evolution under controlled experimental conditions can provide a better understanding of the consequences of evolutionary processes with and without immunological pressure. Characterization of evolved strains assists in the development of predictive algorithms for both the selection of subtypes represented in the seasonal influenza vaccine and the design of novel immune refocused vaccines. To obtain data on the evolution of influenza in a controlled setting, naïve and immunized Guinea pigs were infected with influenza A/Wyoming/2003 (H3N2). Virus progeny from nasal wash samples were assessed for variation in the dominant and other epitopes by sequencing the hemagglutinin (HA) gene to quantify evolutionary changes. Viral RNA from the nasal washes from infection of naïve and immune animals contained 6% and 24.5% HA variant sequences, respectively. Analysis of mutations relative to antigenic epitopes indicated that adaptive immunity played a key role in virus evolution. HA mutations in immunized animals were associated with loss of glycosylation and changes in charge and hydrophobicity in and near residues within known epitopes. Four regions of HA-1 (75–85, 125–135, 165–170, 225–230) contained residues of highest variability. These sites are adjacent to or within known epitopes and appear to play an important role in antigenic variation. Recognition of the role of these sites during evolution will lead to a better understanding of the nature of evolution which help in the prediction of future strains for selection of seasonal vaccines and the design of novel vaccines intended to stimulated broadened cross-reactive protection to conserved sites outside of dominant epitopes.

A multi-scale model for correlation in B cell VDJ usage of zebrafish.

The zebrafish (Danio rerio) is one of the model animals used for the study of immunology because the dynamics in the adaptive immune system of zebrafish are similar to that in higher animals. In this work, we built a multi-scale model to simulate the dynamics of B cells in the primary and secondary immune responses of zebrafish. We use this model to explain the reported correlation between VDJ usage of B cell repertoires in individual zebrafish. We use a delay ordinary differential equation (ODE) system to model the immune responses in the 6-month lifespan of a zebrafish. This mean field theory gives the number of high-affinity B cells as a function of time during an infection. The sequences of those B cells are then taken from a distribution calculated by a microscopic random energy model. This generalized NK model shows that mature B cells specific to one antigen largely possess a single VDJ recombination. The model allows first-principle calculation of the probability, p, that two zebrafish responding to the same antigen will select the same VDJ recombination. This probability p increases with the B cell population size and the B cell selection intensity. The probability p decreases with the B cell hypermutation rate. The multi-scale model predicts correlations in the immune system of the zebrafish that are highly similar to that from experiment.