Article:

Children get the flu more often than adults do, a new study finds. 
  
 Kids typically catch the flu once every two years, whereas adults over age 30 get the flu about twice a decade, a study of people living in China found. 
  
 "There's a lot of debate in the field as to how often people get flu, as opposed to flu-like illness caused by something else," said Adam Kucharski, the study's lead researcher and a fellow at the London School of Hygiene &Tropical Disease in the United Kingdom. Some people who think they have the flu may actually have a cold, whereas other people may truly have the flu and not know it, he said. [6 Flu Vaccine Myths] 
  
 To know for sure whether someone had the flu, researchers search the blood for antibodies against the flu virus. 
  
 When people get sick with the flu, their immune systems produce antibodies that target proteins on the virus surface, and after a person recovers, "the infection will show up when a blood sample is subsequently tested," Kucharski said. In other words, the blood retains a memory of the flu strains that have infected a person in the past. 
  
 In the study, the researchers focused on influenza A (H3N2), a dominant strain of flu virus that sickens and kills the most people worldwide, Kucharski said in a statement. They looked at blood samples from 151 people living in southern China, testing each one against a panel of nine strains of H3N2 that were in circulation between 1968 and 2009, according to the study published today (March 3) in the journal PLOS Biology. 
  
 By comparing people's antibodies with the dates that each flu strain was known to circulate, the researchers were able to figure out how often, on average, people really get the flu. This is the first time that anyone has reconstructed a group's history of flu infection from modern-day blood samples, Kucharski said. 
  
 It's unclear why children are sickened with the flu more often than adults, but the scientists had several ideas. Perhaps children and teenagers interact with more people, and so they may be exposed to more germs, said Stephen Riley, the study's senior researcher and a faculty member at the Medical Research Council Centre for Outbreak Analysis and Modeling at Imperial College London in the United Kingdom. 
  
 "The exact frequency of infection will vary depending on background levels of flu and vaccination," Riley said. 
  
 The researchers, from the U.K., the United States and China, also examined how flu immunity changes in people as they age and encounter different strains of the virus. 
  
 Using a mathematical model, they found that people have stronger immune responses to the flu earlier in life than they do as they get older — a finding that is supported by other studies, Kucharski said. 
  
 "It does seem to be the infections you see later in life, you don't have as great an immune response to," he told Live Science. 
  
 The findings will help scientists understand how a population's immunity influences the flu as the virus evolves, and may help researchers predict how the circulating strains of virus may change in the future, the researchers said. It may also help researchers examine how immunity to historical strains interacts with new vaccines, and how effective new vaccines may be. 
  
 "What we’ve done in this study is to analyze how a person's immunity builds up over a lifetime of flu infections," Kucharski said. "This information helps us understand the susceptibility of the population as a whole, and how easy it is for new seasonal strains to spread through the population." 
  
 Follow Laura Geggel on Twitter @LauraGeggel. Follow Live Science @livescience, Facebook & Google+. Original article on Live Science. ||||| Abstract The immunity of a host population against specific influenza A strains can influence a number of important biological processes, from the emergence of new virus strains to the effectiveness of vaccination programmes. However, the development of an individual’s long-lived antibody response to influenza A over the course of a lifetime remains poorly understood. Accurately describing this immunological process requires a fundamental understanding of how the mechanisms of boosting and cross-reactivity respond to repeated infections. Establishing the contribution of such mechanisms to antibody titres remains challenging because the aggregate effect of immune responses over a lifetime are rarely observed directly. To uncover the aggregate effect of multiple influenza infections, we developed a mechanistic model capturing both past infections and subsequent antibody responses. We estimated parameters of the model using cross-sectional antibody titres to nine different strains spanning 40 years of circulation of influenza A(H3N2) in southern China. We found that “antigenic seniority�? and quickly decaying cross-reactivity were important components of the immune response, suggesting that the order in which individuals were infected with influenza strains shaped observed neutralisation titres to a particular virus. We also obtained estimates of the frequency and age distribution of influenza infection, which indicate that although infections became less frequent as individuals progressed through childhood and young adulthood, they occurred at similar rates for individuals above age 30 y. By establishing what are likely to be important mechanisms driving epochal trends in population immunity, we also identified key directions for future studies. In particular, our results highlight the need for longitudinal samples that are tested against multiple historical strains. This could lead to a better understanding of how, over the course of a lifetime, fast, transient antibody dynamics combine with the longer-term immune responses considered here. 
  
 Author Summary Host immunity against seasonal influenza viruses influences the emergence of new virus strains, the size and severity of “flu�? epidemics, and the effectiveness of vaccination programmes. However, the specific factors that shape the immune response of a single human to a particular strain are little understood because individual infections and the development of immunity over a lifetime in that person are rarely observed directly. To determine the aggregate effect of a lifetime of influenza infections on host immunity, we developed a mathematical model that captures the specific strains with which an individual has been infected and for the corresponding antibody response, the relative contribution of boosting, cross-reactivity, and antigenic seniority to its neutralising ability. Combining the model with data from a survey in southern China that examined antibody levels against nine different influenza strains from 1968 to 2009, we revealed key components of the immune response to influenza virus infection, and obtained estimates of the frequency of influenza infection and the ages at which infection occurred. Our results suggest that “antigenic seniority�?, whereby strains encountered earlier in life gain more “senior�? positions in the immune response, and short-lived cross-reactivity between different strains are important components of the immune response and, therefore, could shape the evolution and emergence of influenza viruses. 
  
 Citation: Kucharski AJ, Lessler J, Read JM, Zhu H, Jiang CQ, Guan Y, et al. (2015) Estimating the Life Course of Influenza A(H3N2) Antibody Responses from Cross-Sectional Data. PLoS Biol 13(3): e1002082. https://doi.org/10.1371/journal.pbio.1002082 Academic Editor: Andrew Fraser Read, The Pennsylvania State University, UNITED STATES Received: April 16, 2014; Accepted: January 16, 2015; Published: March 3, 2015 Copyright: © 2015 Kucharski et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited Data Availability: Data on observed individual titres are available as Dataset S1 in DOI: 10.1371/journal.ppat.1002802. Model estimated titres are available in S1 Data. Funding: This work is supported by the Medical Research Council (UK, Project MR/J008761/1); Wellcome Trust (UK, Project 093488/Z/10/Z); Fogarty International Centre (USA, R01 TW008246-01); Fogarty International Centre with the Science & Technology Directorate, Department of Homeland Security (USA, RAPIDD program); National Institute for General Medical Sciences (US, MIDAS U01 GM110721-01); and National Institute for Health Research (UK, for Health Protection Research Unit funding). JL's work was supported by a grant from NIAID (K22 AI092150-01). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing interests: The authors have declared that no competing interests exist. Abbreviations:: HA, haemagglutinin; HAI, haemagglutination inhibition; NA, neuraminidase 
  
 Introduction The immunity of a host population against specific influenza A strains can influence a number of important biological processes. It can affect the emergence of new virus strains, and hence shape the evolution of the disease [1,2]. It can also influence the size and severity of a pandemic [3–6], and the effectiveness of vaccination programmes [7]. There are two main ways to measure the adaptive immune response against influenza viruses [8]. In microneutralisation assays, a mixture of virus and diluted serum is used to infect cell cultures; the titre is the highest dilution for which virus infection is blocked. Microneutralisation titres therefore measure the overall neutralising antibody response. Such a response can include several components. Some antibodies are specific to antigenic sites on the globular head of the haemagglutinin (HA) surface protein. These sites are highly variable: the HA undergoes frequent mutation, enabling the virus to escape existing antibody responses [9]. There is also evidence that antibodies target conserved epitopes on the stalk of the HA protein or the neuraminidase (NA) surface protein [10–12]. Alternatively, haemagglutination inhibition (HAI) assays measure the extent to which antibodies inhibit binding of the HA protein to red blood cells. Whereas microneutralisation titres likely capture more of the total antibody response, the HAI assay is a more sensitive measure of antibodies that are specific for antigenic sites on the head of the HA protein [13]. The ability of human sera to neutralise current and historical influenza strains exhibits substantial variation between individuals and with age [10,13–15]. These patterns are likely to be influenced by a number of factors. First, neutralisation titres to a particular strain depend on the immune response following exposure to that virus: after infection or vaccination, the immune response to a particular virus can be boosted [5]. Although the initial response may decay to a lower level after a short period of time [16,17], there is evidence that the subsequent level of response can persist for several decades [18]. Observed titres can also depend on the order and number of influenza infections. Francis [14] coined the term “original antigenic sin�? to describe the phenomenon by which HAI titres to the first influenza infection of a lifetime were apparently higher than titres to other strains. Upon subsequent infection, it has been suggested that the original response can be enhanced [14,19–21] and the antibody response to the new strain reduced [22–26]; for original antigenic sin to occur, there is evidence that the original and new strain must be antigenically related [26,27]. Recent work has refined the original antigenic sin hypothesis, proposing that serological patterns should be described in terms of “antigenic seniority�? [13,15]. As with original antigenic sin, the primary infection gains the most “senior�? position in the immune response, but—as a key refinement to the original sin hypothesis—the hierarchy of responses continues with each subsequent infection, as each strain takes a less senior position in the response. As well as boosting and original antigenic sin/antigenic seniority, observed serological responses can also depend on cross-reactivity between strains and temporal waning of responses. Even if hosts have not been exposed to a given strain, they can have a raised titre against the virus if the test strain is similar to those already encountered [5,18]. Establishing the contribution of different mechanisms to neutralisation titres remains challenging, however, because the aggregate effect of immune responses over a lifetime are rarely observed directly [13,24]. Moreover, observed titres not only depend on the relationship between infection and immune response: they are also influenced by the specific strains a host has been infected with. It has been suggested that certain age groups are infected more often than others [28,29], but the true frequency of influenza infection cannot be easily measured [30]. To explore the effects of past infections and subsequent immune responses on observed microneutralisation titres, we fitted a mechanistic model of within-host serological dynamics to data from a cross-sectional survey based in southern China [31]. In the study, 151 individuals’ sera were tested against a panel of nine different influenza A(H3N2) strains isolated between 1968 and 2008. Six of these strains corresponded to representative viruses from every second “antigenic cluster�? that appeared between 1968 and 2003; in total there were 11 such clusters of antigenically similar strains during this period [32]. The other three test viruses were strains that circulated in southern China between 2003 and 2008. We used the mechanistic model to assess the relative contribution of boosting, cross-reactivity, and antigenic seniority to observed neutralisation titres, and estimated key immunological parameters. We also estimated which specific strains each individual had been infected with, and hence assembled infection histories for each individual in the study population. This made it possible to calculate the frequency of infection for influenza A(H3N2) in the host population, and to establish how the infection rate varied with age. 
  
 Materials and Methods Ethics Statement Study protocols and instruments were approved by the following institutional review boards: Johns Hopkins Bloomberg School of Public Health, University of Liverpool, University of Hong Kong, Guangzhou No. 12 Hospital, and Shantou University. Written informed consent was obtained from all participants over 12 y of age, and verbal assent was obtained from participants 12 y of age or younger. Written permission of a legally authorised representative was obtained for all participants under the age of 18 y. Data Participants were recruited from five study locations, with 20 households randomly selected in each location (further details given in Lessler et al. [31]). Participants’ sera were tested against nine representative influenza A(H3N2) strains using a virus neutralisation assay. The strains included six vaccine strains: A/Hong Kong/1/1968, A/Victoria/3/1975, A/Bangkok/1/1979, A/Beijing/353/1989, A/Wuhan/359/1995, and A/Fujian/411/2002. Three strains that circulated in southern China in the years preceding the study were also tested: A/Shantou/90/2003, A/Shantou/806/2005, and A/Shantou/904/2008. Titres were measured using serial 2-fold dilutions from 1:10 to 1:1,280 in duplicate. In our analysis, we represented the results in terms of log neutralisation titres. A log titre of c corresponded to a dilution of 10 × 2(c−1). Hence, there were nine possible log titres: the lowest was 0, which corresponded to a dilution <1:10; the highest was 8, which corresponded to a dilution of 1:1,280. Model of Serological Dynamics We took an “epochal�? view of infection [32,33], with individuals either infected or not during each antigenic epoch; we assumed there were 14 such epochs between 1968 and 2008. We modelled serological titres by assuming that the mean neutralisation titre to a specific strain depended on both individual infection history and a combination of serological mechanisms. We considered four specific mechanisms: boosting from infection with the test strain, cross-reactivity from antigenically similar strains, boosting of earlier responses as a result of subsequent infection, and suppression of subsequent responses as a result of prior immunity. The final two mechanisms have been suggested as potential explanations for observed patterns of antigenic seniority [13,15]. We included the four main mechanisms in the model as follows. Suppose an individual has an infection history that consists of a set of strains X (note that we do not distinguish between live infection and vaccination in the model). We assumed that if an individual had been infected with only one strain, they would exhibit a fixed log titre against that strain, controlled by a single parameter, μ. In the absence of antigenic seniority or cross-reactivity, the individual would therefore have titre equal to μ for every strain in their infection history, and zero for all other strains (Fig. 1A). However, if the individual had been infected with more than one strain, titres against earlier strains could be higher than those against later strains as a result of antigenic seniority. PPT PowerPoint slide 
  
 PowerPoint slide PNG larger image 
  
 larger image TIFF original image Download: Fig 1. Schematic of mechanisms that shape observed titres in the model. (A) Simple boosting. In the absence of cross-reactivity and antigenic seniority, if an individual had been infected with a particular strain, they exhibited a fixed response to that strain equal to μ. This was controlled by a single parameter in the model. In the figure, strains are sorted by date of isolation, with serological samples taken in present day. Strains the host has been infected with are shown in red; coloured bars show the magnitude of observed log titre as a result of past infection with each strain. (B) Boosting of prior responses via antigenic seniority. Infections boosted observed titres to earlier infecting strains by a certain scaling factor, controlled by the parameter τ1. The magnitude of titre to a particular strain therefore depends on the number of infections that occurred after infection with that strain. (C) Suppression of new responses via antigenic seniority. The response to each strain was reduced as a result of immunity generated by previous infections. This reduction was controlled by the parameter τ2. The titre to a particular strain therefore depended on the number of infections that occurred before that strain circulated. (D) Cross-reactivity. In the absence of antigenic seniority, the observed titre to a test strain depended on the response as a result of infection with that strain, plus cross-reactive responses from infection with other strains. These cross-reactive responses decreased with the distance (measured in years) between each infection and the test strain. Strains that circulated further from the test strain in time contributed less to the observed response. https://doi.org/10.1371/journal.pbio.1002082.g001 Two mechanisms have been proposed to explain observed patterns of antigenic seniority: previous responses might be boosted as a result of subsequent infections, or subsequent responses might be reduced as a result of previous immunity [15]. To evaluate the contribution of these two mechanisms, we specified the model so that—depending on parameter values—both, one, or neither mechanism could contribute to measured titres. During the fitting process, model outputs could therefore be compared to observed serological data to establish which mechanism(s) were most plausible given the data. First, we assumed each infection could boost titres against strains encountered previously by a parameter τ 1 (Fig. 1B). Hence the titre μ was scaled by a factor s 1 (X, j) = (1 + τ 1 )|X|−N j where N j is the number of the strain in the infection history (i.e., the first strain is 1, the second is 2, etc.) and |X| is the total number of infections. If τ 1 = 0, then there was no boosting as a result of subsequent infection. This mechanism, in which observed titres to a particular strain depended on the number of subsequent infections, was also proposed by Miller et al. [13] following a longitudinal study of influenza A infections. Second, we assumed prior immunity could reduce observed titres against strains encountered later in life (Fig. 1C). The titre against a particular strain would therefore be scaled by a factor . Here, observed titres to each strain depended on how many infections had occurred previously. When τ 2 = 0, prior infections did not lead to reduced responses against later strains. When τ 2 was large, the formulation was equivalent to a model of original antigenic sin, in which immunity from the primary infection suppressed all subsequent responses [28,34]. Finally, we incorporated cross-reactivity by assuming that mean titre against a specific strain was equal to the sum of cross-reactive responses to all strains in an individual’s infection history. We assumed that the contribution made by each strain depended on the temporal distance between the strain in the infection history and the test strain (Fig. 1D). The level of cross-reaction between a test strain j and infecting strain m was given by d(j, m) = e−σ|t m −t j |, where |t m − t j | was the number of years between strains j and m, and σ was a parameter to be fitted. If σ was large, it was equivalent to having no cross-reactivity between strains. To combine the four mechanisms in the model, we assumed that the log titre individual i has against a strain j was Poisson distributed with the following mean: (1) As d(j, m), s 1 (X, m), or s 2 (X, m) could equal 1 for certain parameter values, the model was capable of omitting certain mechanisms if necessary. We also accounted for potential observation error by assuming that there was a uniform probability of observing a titre different to the true one. Hence, the likelihood of observing titre c j against test strain j was equal to the sum over all possible true titres: (2) We estimated model parameters using Markov chain Monte Carlo (details in S1 Text; dataset and model outputs in S1 Data). As a sensitivity analysis, we also included the waning of antibody responses in our model. This was achieved by modifying Equation 1: (3) where w was a waning parameter that we fixed. The formulation meant that waning reduced titres to strains in the infection history by a factor e−w per year. If w = 0, then we recovered the model given by Equation 1. 
  
 Discussion We have examined how past infections with influenza A(H3N2) strains influence observed cross-sectional neutralisation titres. We found that “antigenic seniority�? and quickly decaying cross-reactivity were important components of the immune response. The order in which an individual is infected with influenza strains was therefore important in dictating observed titres to a particular virus: titres appeared to be the result of a combination of strain-specific boosting, cross-reactivity, and suppression of subsequent responses as a result of antigenic seniority (Fig. 5). PPT PowerPoint slide 
  
 PowerPoint slide PNG larger image 
  
 larger image TIFF original image Download: Fig 5. Schematic of mechanisms that shape observed titres. Our model suggests that the expected magnitude of titres that result from a sequence of infections depends on three of the four mechanisms described in Fig. 1: simple boosting, suppression of subsequent responses as a result of antigenic seniority, and cross-reaction. The contribution from infecting strains to observed titres is influenced by simple boosting and suppression via antigenic seniority (A). These contributions, as well as cross-reaction between similar strains, influence final observed titres (B). For illustrative purposes, strains here appear in 3-y-long epochs, and have circulated over a 40-y period. Strains the host has been infected with are shown in red; coloured bars show the magnitude of observed log titre as a result of past infection with each strain. https://doi.org/10.1371/journal.pbio.1002082.g005 Our results emphasise the importance of understanding how currently unobserved mechanisms shape the dynamics of influenza for individuals over the course of their lifetime. Traditionally, analysis of serological data has been descriptive rather than mechanistic. It has therefore been challenging to distinguish between different hypotheses that could describe observed patterns. In particular, we evaluated two antigenic seniority mechanisms that have been proposed as explanations for why individuals exhibit raised titres to strains encountered earlier in life [15]: earlier responses could be boosted by subsequent infections, or subsequent responses could be reduced as a result of prior immunity. There is an apparent discrepancy between our main parameter estimates for antigenic seniority and empirical observations of boosting of antibodies to early infections. Our baseline results (in the absence of waning) suggest that while there is a reduction in the magnitude of response to later infections, the boosting component of antigenic seniority has a negligible effect on observed long-term titres. However, several studies have found evidence for boosting of existing responses following influenza infection [13,14,19–21]. There were two mechanisms in our model that could potentially lead to increased titres to previously encountered strains after subsequent infections. The first was cross-reaction: in our framework, individuals who had been infected with few strains (Fig. 3A) had lower titres than those who had been infected with several strains that are antigenically related (Fig. 3C). We assumed cross-reaction was symmetric in the model, and made the same contribution to titres regardless of infection order. In contrast, we did not find evidence for boosting of earlier responses as a result of antigenic seniority. In order to investigate this further, we considered the possibility of waning antibodies as a sensitivity analysis. Unfortunately, with cross-sectional data, the boosting and waning processes were not identifiable. Therefore, we assumed a plausible single overall rate of antibody waning and found evidence for boosting as part of an antigenic seniority process. However, although it was possible to force boosting into the model, we suggest that the identifiability issues between boosting and waning and the discrepancy between the model parameters and observed boosting are both consequences of the different timescales on which these immunological processes occur. Boosting and waning are both likely to contribute to the hierarchal nature of antibody responses to influenza. But while waning of elevated titres has been observed over periods of less than a year [35–38], it is not clear precisely how boosted antibody responses persist over time in the absence of infection [41]. Based on our model results, we suggest that repeated boosting of long-lived antibody responses in the absence of waning is unlikely: such a process would lead to either extremely high titres in older individuals or very low rates of infection, neither of which seem credible. Therefore, in essence, we believe our model provides a plausible description of the acquisition of a stable set of persistent antibodies. The apparent discrepancies between observed boosting and relatively low titres to historical strains further highlight the need for studies that take repeated measurements of the serological response of individuals against a panel of historical influenza strains [42]. With such data, the mechanistic model presented here could be expanded to explore both the short- and long-term dynamics of influenza immunity. This would help elucidate the precise role of boosting and suppression in antigenic seniority. We found that cross-reactivity decayed quickly with time, with a half-life of 2.4 y. Hence, there was little cross-reaction between influenza A(H3N2) strains that circulated several years apart. We also considered a model that included broad cross-reactivity between strains, but when we fitted this model to data, the parameter estimate for broad cross-reactivity was zero, indicating that this additional component was not necessary to reproduce observed serological patterns. However, there is evidence that individuals are capable of producing broadly cross-reactive antibodies following infection with a pandemic strain [12,40], and that individuals can exhibit a longitudinal increase in neutralising titres against pandemic strains that are no longer circulating [13]. Again, this highlights the need for longitudinal studies of serological responses against a panel of historical influenza strains. Such data would make it possible to jointly examine the contribution of broad and strain-specific immune responses, and understand how cross-reactive antibodies and antigenic seniority influence observed serological patterns over multiple timescales. As well as comparing the effects of different immune mechanisms, we estimated infection histories for each individual in our study population. We used this information to measure how frequency of infection varied with age. Although infections became less frequent as individuals progressed through childhood and young adulthood, they occurred at similar rates for individuals above age 30 y (Fig. 4A). It has been suggested that influenza transmission is driven by intense social contacts among younger age groups [43]. The decline in frequency of infection with age may be the result of age-specific differences in social behaviour. A study conducted in the same area of southern China as our serological survey found intense mixing within the under-20-y age groups, which could mean the force of infection was higher within these groups [44]. Unfortunately, we had limited serological data for very young individuals (the youngest participant in the study was aged 7 y); it would be interesting to see how the frequency of infection changes from birth through childhood. There are some additional limitations to the work described here. We made no prior assumptions about different age groups’ rate of infection, and hence infection history, in the model. An important next step would be to develop an approach that could measure force of infection from cross-sectional data [45]. This could be explored using a model that accounted for population transmission dynamics as well as serological responses. Moreover, we examined serological data from only 151 participants in southern China. It would therefore be helpful to test similar models of serodynamics against observed titres in other populations [42]. We also focused on responses against a panel of A(H3N2) influenza strains. Unlike group 1 influenza viruses such as A(H1N1), A(H1N1p), and A(H2N2), no group 2 viruses other than A(H3N2) have caused a pandemic; it has been suggested that this is why antibody titres specific to HA stalks might be lower for group 2 viruses than for the more antigenically diverse set of group 1 viruses that have circulated in humans [13]. We also do not distinguish between live infection and vaccination in the model; different routes of exposure could influence the process of antigenic sin/seniority in different ways [12,24]. The model we present offers a novel method for simultaneously investigating immune responses and past infections. Studies looking at the antigenic relationship between different influenza strains typically examine cross-reactivity using ferret sera [9,32]. However, the transmission dynamics of influenza is mediated not just by antigenic change in the virus, but also by underlying immunity in the host population. To analyse the evolutionary trajectory of influenza viruses using human sera, it would be necessary to account for past infections, and how these shape the immune response. We propose that a model of serodynamics, as outlined in this paper, would provide the theoretical foundation required to tackle this problem. Our results also have implications for the analysis of control measures. By considering how a lifetime of infection shapes cross-sectional sera, we have measured the relative importance of different immune mechanisms and past infections in measured serological responses to influenza. As well as influencing the evolution of influenza, such mechanisms could have an impact on the effectiveness of vaccination programmes [7]. We found that the model had limited capacity to accurately predict the magnitude of observed titres to strains that circulated several years before or after the strains to which the model was fitted (S8 Fig.). This is likely the result of the fast decay in cross-reactivity between strains over time. However, the model could generally predict age-specific trends in titres to unencountered strains, even those far from the strain used for fitting. It also reproduced the observed titre levels accurately when fitted strains were close to the test strains in time. This suggests that sufficient representation of past strains, perhaps from every antigenic epoch, would be needed to reproduce all responses accurately. Further, our results were based on neutralisation titres. Similar results are likely to be obtained using HAI, albeit with lower specificity for lower titre values [15,42]. Also, future studies may be able to take advantage of emerging immunological technology based on high throughput protein microarrays [46] and sequence-based measures of B cell diversity [47]. Using a model of cross-sectional serological responses, we have assessed the relative importance of different immune mechanisms and the timing of influenza infection in shaping observed neutralisation titres across the lifetime of an individual. To our knowledge, these two key factors have not previously been combined to fit immunological data. As well as characterising different aspects of the immune response, we have generated individual-level estimates of the frequency and age distribution of influenza infection from cross-sectional serological data. These results demonstrate the value of interpreting immune responses in the context of a lifetime of infection. Integrating the life course of immunity into future analyses of influenza dynamics could therefore lead to a better understanding of population susceptibility and the potential transmissibility of new seasonal strains. 
  
 Author Contributions Conceived and designed the experiments: AJK SR. Performed the experiments: AJK. Analyzed the data: AJK SR. Contributed reagents/materials/analysis tools: JL JMR HZ CQJ YG DATC SR. Wrote the paper: AJK JL JMR DATC SR. Designed the empirical study and collected the data: JL JMR HZ CQJ YG DATC SR. |||||
What is a summary?
– Historically, it's been challenging to pinpoint how often people get the flu, in part because most of us can't self diagnose it, thinking we have it when we don't or not knowing we have it when we do. Now researchers say they've found the answer—that kids get the flu on average every two years, adults every five—by studying antibodies in the blood samples of 151 people in southern China to sort out which influenza strains people have become immune to and when. By comparing a person's antibodies against certain strains with the dates those strains circulated, the researchers were able to ascertain when people actually had the flu, the team reports in the journal PLoS Biology. "The exact frequency of infection will vary depending on background levels of flu and vaccination," one researcher tells LiveScience. The researchers, from China, the US, and the UK, say there are several possible reasons why kids get the flu more than twice as frequently as adults, among them being that they are exposed to more germs due to increased interaction with others, and also that they've simply not lived as long and haven't accumulated the same antibodies against influenza strains. Interestingly, though, kids have stronger immune responses to the flu than adults. The findings could help researchers better predict how strains will adapt in the future, as well as how immunity to past strains may influence one's response to new vaccines. (This year's flu vaccine was one of the least effective in the past decade.)
Artikel: Barn får influensa oftare än vuxna gör, en ny studie finner. Barn drabbas vanligtvis av influensa en gång vartannat år, medan vuxna över 30 år får influensa ungefär två gånger per decennium, en studie av människor som bor i Kina hittade. "Det råder en hel del debatt inom området om hur ofta människor får influensa, i motsats till influensaliknande sjukdom orsakad av något annat", säger Adam Kucharski, studiens huvudforskare och en kollega vid London School of Hygiene & Tropical Disease i Storbritannien. En del människor som tror att de har influensa kan faktiskt ha en förkylning, medan andra människor verkligen kan ha influensa och inte vet det, sade han. För att veta säkert om någon hade influensa, söker forskare i blodet efter antikroppar mot influensaviruset. När människor blir sjuka av influensa, deras immunförsvar producerar antikroppar som målproteiner på virusytan, och efter att en person återhämtar sig, "infektionen kommer att dyka upp när ett blodprov testas senare," Kucharski sade. Med andra ord behåller blodet ett minne av de influensastammar som tidigare har smittat en person. I studien fokuserade forskarna på influensa A (H3N2), en dominant stam av influensavirus som sjukar och dödar flest människor världen över, sade Kucharski i ett uttalande. De tittade på blodprover från 151 personer som bor i södra Kina, och testade var och en mot en panel av nio stammar av H3N2 som var i omlopp mellan 1968 och 2009, enligt den studie som publicerades idag (3 mars) i tidskriften PLOS Biology. Genom att jämföra människors antikroppar med de datum som varje influensastam var känd för att cirkulera kunde forskarna räkna ut hur ofta i genomsnitt människor verkligen får influensa. Detta är första gången som någon har rekonstruerat en grupps historia av influensainfektion från moderna blodprover, sade Kucharski. Det är oklart varför barn blir sjuka av influensa oftare än vuxna, men forskarna hade flera idéer. Kanske barn och tonåringar interagerar med fler människor, och därför kan de utsättas för fler bakterier, sade Stephen Riley, studiens seniorforskare och en fakultetsmedlem vid Medical Research Council Centre for Outbreak Analysis and Modeling vid Imperial College London i Storbritannien. "Den exakta frekvensen av infektion kommer att variera beroende på bakgrundsnivåer av influensa och vaccination," Riley sade. Forskarna, från Storbritannien, USA och Kina, undersökte också hur influensaimmunitet förändras hos människor när de åldras och stöter på olika stammar av viruset. Med hjälp av en matematisk modell fann de att människor har starkare immunsvar mot influensan tidigare i livet än de gör när de blir äldre — ett fynd som stöds av andra studier, sade Kucharski. "Det verkar vara infektionerna du ser senare i livet, du har inte lika bra immunsvar på", sa han till Live Science. Resultaten kommer att hjälpa forskare att förstå hur en populations immunitet påverkar influensan när viruset utvecklas, och kan hjälpa forskare att förutsäga hur de cirkulerande stammarna av virus kan förändras i framtiden, sade forskarna. Det kan också hjälpa forskare att undersöka hur immunitet mot historiska stammar interagerar med nya vacciner, och hur effektiva nya vacciner kan vara. "Vad vi har gjort i denna studie är att analysera hur en persons immunitet bygger upp under en livstid av influensainfektioner," Kucharski sade. "Denna information hjälper oss att förstå känsligheten hos befolkningen som helhet, och hur lätt det är för nya säsongsbetonade stammar att sprida sig över befolkningen." Följ Laura Geggel på Twitter @LauraGeggel. Följ Live Science @livescience, Facebook & Google+. Originalartikel om Levande vetenskap. Sammanfattning Immuniteten hos en värdpopulation mot specifika influensa A-stammar kan påverka ett antal viktiga biologiska processer, från uppkomsten av nya virusstammar till vaccinationsprogrammens effektivitet. Utvecklingen av en individs långvariga antikroppssvar på influensa A under en livstid är dock fortfarande dåligt förstådd. För att exakt beskriva denna immunologiska process krävs en grundläggande förståelse för hur mekanismerna för att öka och korsreaktiviteten svarar på upprepade infektioner. Att fastställa hur sådana mekanismer bidrar till antikroppstitrar är fortfarande en utmaning eftersom den samlade effekten av immunsvar över en livstid sällan observeras direkt. För att avslöja den samlade effekten av flera influensainfektioner utvecklade vi en mekanistisk modell som fångar både tidigare infektioner och efterföljande antikroppssvar. Vi uppskattade parametrar för modellen med hjälp av tvärsnittsantikroppstitrar till nio olika stammar som sträcker sig över 40 års cirkulation av influensa A(H3N2) i södra Kina. Vi fann att “antigen senioritet ? och snabbt sönderfallande korsreaktivitet var viktiga komponenter i immunsvaret, vilket tyder på att den ordning i vilken individer infekterades med influensastammar formade observerade neutralisationstitrar till ett visst virus. Vi fick också uppskattningar av frekvensen och åldersfördelningen av influensainfektion, vilket indikerar att även om infektioner blev mindre vanliga när individerna gick framåt genom barndomen och unga vuxen ålder, inträffade de i liknande takt för individer över 30 år. Genom att fastställa vad som sannolikt kommer att vara viktiga mekanismer som driver epokgörande trender i befolkningsimmunitet, identifierade vi också viktiga riktningar för framtida studier. Våra resultat belyser särskilt behovet av longitudinella prover som testas mot flera historiska stammar. Detta kan leda till en bättre förståelse av hur snabb, övergående antikroppsdynamik under en livstid kombineras med de långsiktiga immunsvar som beaktas här. Författare Sammanfattning Värd immunitet mot säsongsinfluensavirus påverkar uppkomsten av nya virusstammar, storleken och svårighetsgraden av “flu ? epidemier och vaccinationsprogrammens effektivitet. De specifika faktorer som formar en enda människas immunsvar mot en viss stam är dock föga förstådda eftersom individuella infektioner och utvecklingen av immunitet under en livstid hos den personen sällan observeras direkt. För att bestämma den samlade effekten av en livstid av influensainfektioner på värdimmunitet, utvecklade vi en matematisk modell som fångar de specifika stammar med vilka en individ har smittats och för motsvarande antikroppsrespons, det relativa bidraget av att öka, korsreaktivitet, och antigenisk senioritet till dess neutraliserande förmåga. Genom att kombinera modellen med data från en undersökning i södra Kina som undersökte antikroppsnivåer mot nio olika influensastammar 1968–2009, avslöjade vi nyckelkomponenter i immunsvaret mot influensavirusinfektion, och fick uppskattningar av frekvensen av influensainfektion och de åldrar då infektionen inträffade. Våra resultat tyder på att “antigen senioritet ?, varvid stammar som påträffats tidigare i livet få mer “senior ? positioner i immunsvaret, och kortlivad korsreaktivitet mellan olika stammar är viktiga komponenter i immunsvaret och kan därför forma utvecklingen och uppkomsten av influensavirus. Citation: Kucharski AJ, Lessler J, Read JM, Zhu H, Jiang CQ, Guan Y, et al. (2015) Uppskattning av influensans livscykel A(H3N2) Antikroppssvar från tvärsnittsdata. PLoS Biol 13.3: e1002082. https://doi.org/10.1371/journal.pbio.1002082 Akademisk redaktör: Andrew Fraser Read, The Pennsylvania State University, USA Mottagen: 16 april 2014; Godkänd: 16 januari 2015; Publicerad: 3 mars 2015 Copyright: © 2015 Kucharski et al. Detta är en open access-artikel som distribueras enligt villkoren i Creative Commons Attribution License, som tillåter obegränsad användning, distribution och reproduktion i alla medier, förutsatt att den ursprungliga upphovsmannen och källan krediteras Data Tillgänglighet: Data om observerade individuella titrar finns tillgängliga som Dataset S1 i DOI: 10.1371/journal.ppat.1002802. Modelluppskattade titrar finns i S1 Data. Finansiering: Detta arbete stöds av Medicinska forskningsrådet (UK, Project MR/J008761/1); Wellcome Trust (UK, Project 093488/Z/10/Z); Fogarty International Centre (USA, R01 TW008246-01); Fogarty International Centre with the Science & Technology Directorate, Department of Homeland Security (USA, RAPIDD-programmet); National Institute for General Medical Sciences (USA, MIDAS U01 GM110721-01); och National Institute for Health Research (UK, for Health Protection Research Unit finance). JL:s arbete stöddes av ett bidrag från NIAID (K22 AI092150-01). Finansiärerna hade ingen roll i studiedesign, datainsamling och analys, beslut att publicera, eller utarbetande av manuskriptet. Konkurrerande intressen: Författarna har förklarat att det inte finns några konkurrerande intressen. Förkortningar: HA, hemagglutinin; HAI, hemagglutinationshämning; NA, neuraminidas Inledning Immuniteten hos en värdpopulation mot specifik influensa A-stammar kan påverka ett antal viktiga biologiska processer. Det kan påverka uppkomsten av nya virusstammar och därmed forma sjukdomens utveckling [1,2]. Det kan också påverka en pandemis storlek och svårighetsgrad [3–6] och vaccinationsprogrammens effektivitet [7]. Det finns två huvudsakliga sätt att mäta det adaptiva immunsvaret mot influensavirus [8]. I mikroneutraliseringstest används en blandning av virus och utspädd serum för att infektera cellkulturer; titern är den högsta utspädning för vilken virusinfektion blockeras. Mikroneutraliseringstitrar mäter därför det totala neutraliserande antikroppssvaret. Ett sådant svar kan innehålla flera komponenter. Vissa antikroppar är specifika för antigena ställen på globulära huvudet av hemagglutinin (HA) ytan protein. Dessa platser är mycket varierande: HA genomgår frekvent mutation, vilket gör att viruset kan undkomma befintliga antikroppssvar [9]. Det finns också belägg för att antikropparna har bevarat epitoper på stjälken av HA-proteinet eller neuraminidas (NA) ytprotein [10–12]. Alternativt mäter hemagglutinationshämningstest (HAI) i vilken utsträckning antikroppar hämmar bindningen av HA-proteinet till röda blodkroppar. Mikroneutraliseringstitrar fångar sannolikt mer av det totala antikroppssvaret, men HAI-testet är ett känsligare mått på antikroppar som är specifika för antigener på huvudet av HA-proteinet [13]. Förmågan hos humant serum att neutralisera nuvarande och historiska influensastammar uppvisar betydande variation mellan individer och med ålder [10,13–15]. Dessa mönster påverkas sannolikt av ett antal faktorer. För det första beror neutraliseringstitrar till en viss stam på immunsvaret efter exponering för detta virus: efter infektion eller vaccination kan immunsvaret mot ett visst virus ökas [5]. Även om det första svaret kan förfalla till en lägre nivå efter en kort tidsperiod [16,17], finns det bevis för att den efterföljande reaktionsnivån kan kvarstå under flera årtionden [18]. Observerade titrar kan också bero på ordningen och antalet influensainfektioner. Frans [14] myntade termen “ursprunglig antigen synd ? att beskriva det fenomen genom vilket HAI-titer till den första influensainfektionen under en livstid uppenbarligen var högre än titer till andra stammar. Vid efterföljande infektion har det föreslagits att det ursprungliga svaret kan förstärkas [14,19–21] och antikroppssvaret på den nya stammen minskas [22–26], för att ursprunglig antigensynd ska uppstå finns belägg för att den ursprungliga och nya stammen måste vara antigeniskt relaterad [26,27]. Nyligen arbete har förfinat den ursprungliga antigena synd hypotesen, föreslår att serologiska mönster bör beskrivas i termer av “antigen senioritet ? [13,15]. Som med original antigen synd, den primära infektionen får mest “senior ? position i immunsvaret, men – som en viktig förfining till den ursprungliga syndhypotesen – hierarkin av svar fortsätter med varje efterföljande infektion, eftersom varje stam tar en mindre senior position i svaret. Förutom att öka och ursprungliga antigensynd/antigen senioritet, kan observerade serologiska svar också bero på korsreaktivitet mellan stammar och temporal avmattning av svar. Även om värdar inte har exponerats för en viss stam kan de ha en upphöjd titrering mot viruset om teststammen liknar de som redan har påträffats [5,18]. Att fastställa olika mekanismers bidrag till neutraliseringstitrar är dock fortfarande en utmaning, eftersom den sammanlagda effekten av immunsvar under en livstid sällan observeras direkt [13,24]. Dessutom beror observerade titrar inte bara på förhållandet mellan infektion och immunsvar: de påverkas också av de specifika stammar en värd har infekterats med. Det har föreslagits att vissa åldersgrupper smittas oftare än andra [ 28,29], men den verkliga frekvensen av influensainfektion kan inte lätt mätas [30]. För att undersöka effekterna av tidigare infektioner och efterföljande immunsvar på observerade mikroneutralisationstitrar, anpassade vi en mekanistisk modell av serologisk dynamik inom värddjuret till data från en tvärsnittsundersökning baserad i södra Kina [31]. I studien testades 151 individers serum mot en panel av nio olika influensa A(H3N2) stammar som isolerats mellan 1968 och 2008. Sex av dessa stammar motsvarade representativa virus från varje sekund “antigena kluster ? De förekom mellan 1968 och 2003; sammanlagt fanns det 11 sådana kluster av antigenliknande stammar under denna period [32]. De övriga tre testvirusen var stammar som cirkulerade i södra Kina mellan 2003 och 2008. Vi använde den mekanistiska modellen för att bedöma det relativa bidraget av att öka, korsreaktivitet, och antigen senioritet till observerade neutralisationstitrar, och uppskattade viktiga immunologiska parametrar. Vi uppskattade också vilka specifika stammar varje individ hade smittats med, och samlade därmed in infektionshistorik för varje individ i studiepopulationen. Detta gjorde det möjligt att beräkna infektionsfrekvensen för influensa A(H3N2) i värdpopulationen, och att fastställa hur infektionsfrekvensen varierade med åldern. Materials and Methods Ethics Statement Studieprotokoll och instrument godkändes av följande institutionella granskningsnämnder: Johns Hopkins Bloomberg School of Public Health, University of Liverpool, University of Hong Kong, Guangzhou No. 12 Hospital, och Shantou University. Skriftligt informerat samtycke erhölls från alla deltagare över 12 år, och muntligt samtycke erhölls från deltagare som var 12 år eller yngre. Skriftligt tillstånd från ett juridiskt behörigt ombud erhölls för alla deltagare under 18 år. Data Deltagare rekryterades från fem studieplatser, med 20 hushåll slumpmässigt utvalda på varje plats (ytterligare uppgifter anges i Lessler et al. [31]). Deltagarnas serum testades mot nio representativa stammar av influensa A(H3N2) med hjälp av en virusneutralisationsanalys. Stammarna inkluderade sex vaccinstammar: A/Hong Kong/1/1968, A/Victoria/35/1975, A/Bangkok/1/1979, A/Beijing/353/1989, A/Wuhan/359/1995 och A/Fujian/411/2002. Tre stammar som cirkulerade i södra Kina under åren före studien testades också: A/Shantou/90/2003, A/Shantou/806/2005 och A/Shantou/904/2008. Titrer mättes med seriella 2-faldiga utspädningar från 1:10 till 1:1,280 i två exemplar. I vår analys representerade vi resultaten i termer av logneutralisationstitrar. En log titer av c motsvarade en utspädning på 10 × 2 c−1). Därför fanns det nio möjliga log titrar: den lägsta var 0, vilket motsvarade en utspädning < 1:10; den högsta var 8, vilket motsvarade en utspädning på 1:1,280. Modell av serologisk dynamik Vi tog en “epochal ? syn på infektion [32,33], med individer antingen smittade eller inte under varje antigenepok; vi antog att det fanns 14 sådana epoker mellan 1968 och 2008. Vi modellerade serologiska titrar genom att anta att medelneutralisationstiter till en specifik stam var beroende av både individuell infektionshistoria och en kombination av serologiska mekanismer. Vi övervägde fyra specifika mekanismer: ökning från infektion med teststammen, korsreaktivitet från antigenliknande stammar, ökning av tidigare svar till följd av efterföljande infektion, och dämpning av efterföljande svar till följd av tidigare immunitet. De två sista mekanismerna har föreslagits som potentiella förklaringar till observerade mönster av antigen senioritet [13,15]. Vi tog med de fyra huvudmekanismerna i modellen enligt följande. Antag att en individ har en infektionshistoria som består av en uppsättning stammar X (observera att vi inte skiljer mellan levande infektion och vaccination i modellen). Vi antog att om en individ hade smittats med endast en stam, skulle de uppvisa en fast log titer mot den stammen, kontrollerad av en enda parameter, μ. I avsaknad av antigen senioritet eller korsreaktivitet skulle individen därför ha titrering lika med μ för varje stam i sin infektionshistoria, och noll för alla andra stammar (Fig. 1A). Om individen hade smittats med mer än en stam kunde titrar mot tidigare stammar dock vara högre än de mot senare stammar till följd av antigen senioritet. PPT PowerPoint slide PowerPoint slide PNG större bild större bild TIFF originalbild Ladda ner: Bild 1. Schematisk över mekanismer som formar observerade titrar i modellen. (A) Enkelt öka. Om en individ hade smittats med en viss stam, visade de i avsaknad av korsreaktivitet och antigen senioritet ett fast svar på denna stam motsvarande μ. Detta styrdes av en enda parameter i modellen. I figuren sorteras stammarna efter isoleringsdatum, med serologiska prover tagna i dag. Stamdjur som värden har smittats med visas i rött; färgade galler visar magnituden av observerad log titer till följd av tidigare infektion med varje stam. (B) Ökning av tidigare svar via antigen seniority. Infektioner boostade observerade titrar till tidigare infekterade stammar med en viss skalfaktor, kontrollerad av parametern τ1. Storleken på titer till en viss stam beror därför på antalet infektioner som inträffade efter infektion med den stammen. (C) Förhindrande av nya reaktioner via antigen senioritet. Svaren på varje stam reducerades till följd av immunitet som genererats av tidigare infektioner. Denna minskning kontrollerades av parametern τ2. Titern till en viss stam var därför beroende av antalet infektioner som inträffade innan denna stam cirkulerade. (D) Korsreaktivitet. I avsaknad av antigen senioritet var den observerade titern till en teststam beroende av svaret på grund av infektion med denna stam, plus korsreaktiva svar från infektion med andra stammar. Dessa korsreaktiva svar minskade med avståndet (mätt i år) mellan varje infektion och teststammen. Stam som cirkulerade längre från teststammen i tid bidrog mindre till det observerade svaret. https://doi.org/10.1371/journal.pbio.1002082.g001 Två mekanismer har föreslagits för att förklara observerade mönster av antigen senioritet: tidigare svar kan förstärkas till följd av efterföljande infektioner, eller senare svar kan minskas till följd av tidigare immunitet [15]. För att utvärdera bidraget från dessa två mekanismer, specificerade vi modellen så att – beroende på parametervärden – både, en, eller ingen av mekanismerna kunde bidra till uppmätta titrar. Under anpassningsprocessen kunde man därför jämföra modellresultaten med observerade serologiska data för att fastställa vilka mekanismer som var mest rimliga med tanke på uppgifterna. Först antog vi att varje infektion kunde öka titrar mot stammar som tidigare stött på av en parameter τ 1 (Fig. 1B). Därför skalades titern μ med en faktor s 1 (X, j) = (1 + τ 1 )°°′′X°′N j där N j är stammens antal i infektionshistorien (dvs. den första stammen är 1, den andra är 2, etc.) och ’’X’’ är det totala antalet infektioner. Om τ 1 = 0, då det inte fanns någon ökning till följd av efterföljande infektion. Denna mekanism, i vilken observerade titrar till en viss stam var beroende av antalet efterföljande infektioner, föreslogs också av Miller et al. [13] efter en longitudinell studie av influensa A-infektioner. För det andra antog vi att tidigare immunitet kunde minska observerade titrar mot stammar som påträffades senare i livet (Fig. 1C). Titern mot en viss stam skulle därför skalas med en faktor . Här berodde observerade titrar till varje stam på hur många infektioner som hade förekommit tidigare. När τ 2 = 0, ledde tidigare infektioner inte till minskat svar mot senare stammar. När τ 2 var stort motsvarade formuleringen en modell av ursprunglig antigensynd, i vilken immunitet mot den primära infektionen undertryckte alla efterföljande svar [28,34]. Slutligen införlivade vi korsreaktivitet genom att anta att medeltiter mot en specifik stam var lika med summan av korsreaktiva svar på alla stammar i en individs infektionshistoria. Vi antog att bidraget från varje stam var beroende av tidsavståndet mellan stammen i infektionshistorien och teststammen (Fig. 1D). Korsreaktionsnivån mellan en teststam j och den infekterande stam m gavs av d(j, m) = e′′t m −t j −′′, där Ø′t m − t j −′ var antalet år mellan stammarna j och m, och σ var en parameter som skulle monteras. Om σ var stort motsvarade det ingen korsreaktivitet mellan stammarna. För att kombinera de fyra mekanismerna i modellen antog vi att logtitren individ i har mot en stam j var Poisson fördelade med följande medelvärde: 1) Som d(j, m), s 1 (X, m), eller s 2 (X, m) kunde motsvara 1 för vissa parametervärden, var modellen kapabel att utelämna vissa mekanismer vid behov. Vi förklarade också ett potentiellt observationsfel genom att anta att det fanns en enhetlig sannolikhet att observera en titrering som skilde sig från den sanna. Sannolikheten för att observera titer c j mot teststam j var därför lika stor som summan över alla möjliga sanna titer: (2) Vi uppskattade modellparametrar med hjälp av Markovkedjan Monte Carlo (detaljer i S1 Text; datauppsättning och modellutgångar i S1 Data). Som en känslighetsanalys inkluderade vi också avtagande antikroppssvar i vår modell. Detta uppnåddes genom att modifiera ekvation 1: (3) där w var en avtagande parameter som vi fixade. Formuleringen innebar att avtagande reducerade titrar till stammar i infektionshistorien med en faktor e−w per år. Om w = 0, då återfanns modellen som gavs genom ekvation 1. Diskussion Vi har undersökt hur tidigare infektioner med influensa A(H3N2) stammar påverkar observerade tvärsnittsneutralisationstitrar. Vi fann att “antigen senioritet ? och snabbt sönderfallande korsreaktivitet var viktiga komponenter i immunsvaret. Den ordning i vilken en individ är smittad med influensastammar var därför viktig för att diktera observerade titrar till ett visst virus: titrar verkade vara resultatet av en kombination av stamspecifik ökning, korsreaktivitet och dämpning av efterföljande svar till följd av antigen senioritet (Fig. 5. PPT PowerPoint slide PowerPoint slide PNG större bild större bild TIFF originalbild Ladda ner: Bild 5. Schematisk över mekanismer som formar observerade titrar. Vår modell tyder på att den förväntade storleken av titrar som blir resultatet av en sekvens av infektioner beror på tre av de fyra mekanismer som beskrivs i Fig. 1: enkel ökning, dämpning av efterföljande svar till följd av antigen senioritet, och korsreaktion. Bidraget från infekterande stammar till observerade titrar påverkas av enkel ökning och suppression via antigen senioritet (A). Dessa bidrag, liksom korsreaktion mellan liknande stammar, påverkar de slutliga observerade titrarna (B). För illustrativa ändamål uppträder här stammar i 3-y-långa epoker, och har cirkulerat under en 40-y-period. Stamdjur som värden har smittats med visas i rött; färgade galler visar magnituden av observerad log titer till följd av tidigare infektion med varje stam. https://doi.org/10.1371/journal.pbio.1002082.g005 Våra resultat betonar vikten av att förstå hur för närvarande obemärkta mekanismer formar influensans dynamik för individer under deras livstid. Traditionellt har analys av serologiska data snarare varit beskrivande än mekanistiskt. Det har därför varit svårt att skilja mellan olika hypoteser som skulle kunna beskriva observerade mönster. I synnerhet utvärderade vi två mekanismer för antigen senioritet som har föreslagits som förklaringar till varför individer uppvisar förhöjda titrar till stammar som påträffats tidigare i livet [15]: tidigare svar kan förstärkas av efterföljande infektioner, eller efterföljande svar kan minskas till följd av tidigare immunitet. Det finns en uppenbar diskrepans mellan våra huvudsakliga parameteruppskattningar för antigen senioritet och empiriska observationer av ökning av antikroppar mot tidiga infektioner. Våra baslinjeresultat (i frånvaro av avtagande) tyder på att även om det finns en minskning i omfattningen av svar på senare infektioner, har den förstärkande komponenten av antigen senioritet en försumbar effekt på observerade långsiktiga titrar. Flera studier har dock funnit belägg för att öka befintliga svar efter influensainfektion [13,14,19–21]. Det fanns två mekanismer i vår modell som potentiellt kunde leda till ökade titrar till tidigare stött på stammar efter efterföljande infektioner. Den första var korsreaktion: inom vårt ramverk, individer som hade smittats med få stammar (Fig. 3A) hade lägre titrar än de som hade smittats med flera stammar som är antigeniskt besläktade (Fig. 3C). Vi antog korsreaktion var symmetrisk i modellen, och gjorde samma bidrag till titrar oavsett infektionsordning. Däremot fann vi inga bevis för att tidigare reaktioner hade ökat till följd av antigen senioritet. För att undersöka detta ytterligare övervägde vi möjligheten av avtagande antikroppar som en känslighetsanalys. Med tvärsnittsdata kunde man tyvärr inte identifiera stegrings- och avmattningsprocesserna. Därför antog vi en rimlig total antikroppsavmattning och fann bevis för att öka som en del av en antigen seniority process. Även om det var möjligt att tvinga in en förstärkning i modellen, föreslår vi dock att identifibilitetsfrågorna mellan ökning och avtagande och diskrepansen mellan modellparametrarna och observerad ökning är båda konsekvenserna av de olika tidsramar som dessa immunologiska processer uppstår på. Det är både troligt att booster och avtagande bidrar till den hierarkala karaktären hos antikroppssvaren mot influensa. Men även om avtagande av förhöjda titrar har observerats under perioder på mindre än ett år [35–38], är det inte klart exakt hur förstärkt antikroppssvar kvarstår över tiden i frånvaro av infektion [41]. Baserat på vår modell resultat, föreslår vi att upprepade ökning av långlivade antikroppssvar i frånvaro av avtagande är osannolikt: en sådan process skulle leda till antingen extremt höga titrar hos äldre individer eller mycket låga smittfrekvenser, ingen av dem verkar trovärdig. Därför anser vi att vår modell ger en rimlig beskrivning av förvärvet av en stabil uppsättning bestående antikroppar. De uppenbara skillnaderna mellan observerade booster och relativt låga titrar till historiska stammar understryker ytterligare behovet av studier som gör upprepade mätningar av individers serologiska svar mot en panel av historiska influensastammar [42]. Med sådana data skulle den mekanistiska modell som presenteras här kunna utvidgas för att utforska både den kortsiktiga och långsiktiga dynamiken hos influensaimmunitet. Detta skulle bidra till att klargöra den exakta rollen av boosting och suppression i antigen seniority. Vi fann att korsreaktiviteten snabbt förföll med tiden, med en halveringstid på 2,4 y. Det förekom därför inte mycket korsreaktion mellan influensa A- (H3N2) stammar som cirkulerade flera år ifrån varandra. Vi övervägde också en modell som inkluderade bred korsreaktivitet mellan stammar, men när vi anpassade denna modell till data var parameteruppskattningen för bred korsreaktivitet noll, vilket tyder på att denna ytterligare komponent inte var nödvändig för att reproducera observerade serologiska mönster. Det finns dock belägg för att individer kan producera i stort sett korsreaktiva antikroppar efter infektion med en pandemistam [12,40], och att individer kan uppvisa en longitudinell ökning av neutraliserande titrar mot pandemistammar som inte längre cirkulerar [13]. Detta belyser återigen behovet av longitudinella studier av serologiska svar mot en panel av historiska influensastammar. Sådana data skulle göra det möjligt att gemensamt undersöka bidraget från breda och stamspecifika immunsvar, och förstå hur korsreaktiva antikroppar och antigen senioritet påverkar observerade serologiska mönster över flera tidsskalor. Förutom att jämföra effekterna av olika immunmekanismer uppskattade vi infektionshistoriken för varje individ i vår studiepopulation. Vi använde denna information för att mäta hur infektionsfrekvensen varierade med åldern. Även om infektioner blev mindre vanliga när individerna gick framåt genom barndomen och unga vuxen ålder, inträffade de i liknande takt för individer över 30 år (Fig. 4A). Det har föreslagits att influensaöverföringen drivs av intensiva sociala kontakter bland yngre åldersgrupper [43]. Minskningen av infektionsfrekvensen med åldern kan bero på åldersspecifika skillnader i socialt beteende. En studie som genomfördes i samma område i södra Kina som vår serologiska undersökning fann intensiv blandning inom åldersgrupperna under 20 år, vilket skulle kunna innebära att infektionskraften var högre inom dessa grupper [44]. Tyvärr hade vi begränsade serologiska data för mycket unga individer (den yngsta deltagaren i studien var 7 år); det skulle vara intressant att se hur infektionsfrekvensen förändras från födseln till barndomen. Det finns ytterligare några begränsningar i det arbete som beskrivs här. Vi gjorde inga tidigare antaganden om olika åldersgruppers infektionsfrekvens, och därmed infektionshistoria, i modellen. Ett viktigt nästa steg skulle vara att utveckla ett tillvägagångssätt som skulle kunna mäta infektionskraften från tvärsnittsdata [45]. Detta skulle kunna undersökas med hjälp av en modell som svarade för såväl populationsöverföringsdynamik som serologiska svar. Dessutom undersökte vi serologiska data från endast 151 deltagare i södra Kina. Det skulle därför vara till hjälp att testa liknande modeller av serodynamik mot observerade titrar i andra populationer [42]. Vi fokuserade också på reaktioner mot en panel av A(H3N2) influensastammar. Till skillnad från influensavirus i grupp 1 som A(H1N1), A(H1N1p) och A(H2N2) har inga andra virus i grupp 2 än A(H3N2) orsakat en pandemi; det har föreslagits att detta är orsaken till att antikroppstitrar som är specifika för HA-stjälkar kan vara lägre för virus i grupp 2 än för de mer antigeniskt varierande virus i grupp 1 som cirkulerat hos människor [13]. Vi skiljer inte heller på levande infektion och vaccination i modellen; olika exponeringsvägar kan påverka processen för antigens sin/senioritet på olika sätt [12,24]. Modellen vi presenterar erbjuder en ny metod för att samtidigt undersöka immunsvar och tidigare infektioner. Studier där man undersöker det antigena sambandet mellan olika influensastammar undersöker vanligtvis korsreaktivitet med illerserum [9,32]. Transmissionsdynamiken hos influensa medieras dock inte bara av antigena förändringar i viruset, utan även av underliggande immunitet i värdpopulationen. För att analysera den evolutionära banan av influensavirus med hjälp av humant serum, skulle det vara nödvändigt att ta hänsyn till tidigare infektioner, och hur dessa formar immunsvaret. Vi föreslår att en modell av serodynamik, som beskrivs i detta dokument, skulle utgöra den teoretiska grund som krävs för att ta itu med detta problem. Våra resultat har också konsekvenser för analysen av kontrollåtgärder. Genom att överväga hur en livstid av infektion formar tvärsnittsserum, har vi mätt den relativa betydelsen av olika immunmekanismer och tidigare infektioner i uppmätta serologiska svar på influensa. Förutom att påverka influensans utveckling kan sådana mekanismer påverka vaccinationsprogrammens effektivitet [7]. Vi fann att modellen hade begränsad kapacitet att exakt förutsäga storleken av observerade titrar till stammar som cirkulerade flera år före eller efter de stammar som modellen var monterad på (S8 Fig.). Detta är sannolikt resultatet av det snabba förfallet i korsreaktivitet mellan stammar över tid. Modellen kunde dock i allmänhet förutsäga åldersspecifika trender i titrar till icke-kontraherade stammar, även sådana som ligger långt från den stam som används för montering. Den återgav också de observerade titreringsnivåerna exakt när stammarna var nära teststammarna i tid. Detta tyder på att det skulle behövas tillräcklig representation av tidigare stammar, kanske från varje antigenepok, för att alla svar skulle kunna reproduceras exakt. Dessutom baserades våra resultat på neutraliseringstitrar. Liknande resultat förväntas uppnås med HAI, om än med lägre specificitet för lägre titrevärden [15,42]. Framtida studier kan också dra nytta av framväxande immunologisk teknik baserad på höggenomströmning av proteinmikroarrayer [46] och sekvensbaserade mått på B-cellsmångfald [47]. Med hjälp av en modell av tvärsektionella serologiska svar har vi bedömt den relativa betydelsen av olika immunmekanismer och tidpunkten för influensainfektion för att forma observerade neutralisationstitrar under en individs livstid. Såvitt vi vet har dessa två nyckelfaktorer inte tidigare kombinerats för att passa immunologiska data. Förutom att karakterisera olika aspekter av immunsvaret har vi genererat individuella uppskattningar av influensainfektionens frekvens och åldersfördelning från tvärsnittsserologiska data. Dessa resultat visar värdet av att tolka immunsvar i samband med en livstid av infektion. Att integrera immunitetens livskurs i framtida analyser av influensadynamiken skulle därför kunna leda till en bättre förståelse av populationskänsligheten och den potentiella överförbarheten av nya säsongsstammar. Författarbidrag Uppfattade och utformade experimenten: AJK SR. Utförde experimenten: AJK. Analyserade data: AJK SR. Bidragda reagenser/material/analysverktyg: JL JMR HZ CQJ YG DATC SR. Skrev tidningen: AJK JL JMR DATC SR. Utformade den empiriska studien och samlade in data: JL JMR HZ CQJ YG DATC SR. <unk> <unk> Vad är en sammanfattning?
Article: Children get flu more often than adults do, a new study finds. Children usually suffer from flu once every two years, while adults over 30 years of age get flu about twice a decade, a study of people living in China found. "There is a lot of debate in the field about how often people get flu, as opposed to flu-like illness caused by something else," said Adam Kucharski, the study's lead researcher and a colleague at the London School of Hygiene & Tropical Disease in the UK. Some people who think they have the flu may actually have a cold, while other people can really have the flu and don't know it," he said. To know for sure if anyone had the flu, researchers in the blood are looking for antibodies against the flu virus. When people get sick from flu, their immune system produces antibodies like target proteins on the viral surface, and after a person recovers, "the infection will appear when a blood test is later tested," Kucharski said. In other words, blood retains a memory of the flu strains that have previously infected a person. In the study, researchers focused on influenza A (H3N2), a dominant strain of influenza viruses that sicken and kill the most people worldwide, said Kucharski in a statement. They looked at blood samples from 151 people living in southern China, and each tested against a panel of nine strains of H3N2 that were in circulation between 1968 and 2009, according to the study published today (March 3) in the journal PLOS Biology. By comparing human antibodies to the dates that each flu strain was known to circulate, scientists were able to figure out how often on average people actually get the flu. This is the first time that anyone has reconstructed a group's history of influenza infection from modern blood tests, Kucharski said. It is unclear why children get sick more often than adults, but the researchers had several ideas. Perhaps children and teenagers interact with more people, and therefore they may be exposed to more bacteria, said Stephen Riley, the study's senior researcher and a faculty member at the Medical Research Council Centre for Outbreak Analysis and Modeling at Imperial College London, United Kingdom. "The exact frequency of infection will vary depending on background levels of influenza and vaccination," Riley said. The researchers, from the United Kingdom, the United States, and China, also investigated how influenza immunity changes in humans as they age and encounter various strains of the virus. Using a mathematical model, they found that people have stronger immune responses to the flu earlier in life than they do when they get older — a finding supported by other studies, Kucharski said. "It seems to be the infections you see later in life, you don't have as good immune responses to," he told Live Science. The results will help researchers understand how a population's immunity affects influenza as the virus develops, and can help researchers predict how the circulating strains of viruses can change in the future," said the researchers. It can also help researchers investigate how immunity to historical strains interacts with new vaccines, and how effective new vaccines can be. "What we've done in this study is analyze how a person's immunity builds up over a lifetime of flu infections," Kucharski said. "This information helps us understand the sensitivity of the population as a whole, and how easy it is for new seasonal tribes to spread across the population." Follow Laura Geggel on Twitter @LauraGeggel. Follow Live Science @livescience, Facebook & Google+. Original article on Living Science. Summary The immunity of a host population against specific influenza A strains may affect a number of important biological processes, from the emergence of new virus strains to the effectiveness of vaccination programs. However, the development of an individual's long-term antibody response to influenza A over a lifetime is still poorly understood. To accurately describe this immunological process requires a basic understanding of how the mechanisms to increase and cross-reactivity respond to repeated infections. Identifying how such mechanisms contribute to antibody titres remains a challenge as the overall effect of immune responses over a lifetime is rarely observed directly. To reveal the overall effect of several influenza infections, we developed a mechanistic model that captures both previous infections and subsequent antibody responses. We estimated parameters for the model using cross-sectional antibody titres to nine different strains that span 40 years of circulation of influenza A(H3N2) in southern China. We found that “antigen seniority? and rapidly decaying cross-reactivity were important components of the immune response, suggesting that the order in which individuals were infected with influenza strains shaped observed neutralization titres to a particular virus. We also received estimates of the frequency and age distribution of influenza infection, indicating that although infections became less common as individuals progressed through childhood and young adulthood, they occurred at a similar rate for individuals over 30 years of age. By determining what are likely to be important mechanisms that drive epoch-making trends in population immunity, we also identified important directions for future studies. Our results highlight in particular the need for longitudinal samples tested against several historical strains. This may lead to a better understanding of how fast, transient antibody dynamics over a lifetime are combined with the long-term immune responses considered here. Author Summary Host immunity against seasonal flu virus affects the emergence of new virus strains, the size and severity of “flu ? epidemics and the effectiveness of vaccination programmes. However, the specific factors that shape a single human's immune response to a particular strain are little understood because individual infections and the development of immunity over a lifetime in that person are rarely observed directly. To determine the overall effect of a lifetime of influenza infections on host immunity, we developed a mathematical model that captures the specific strains with which an individual has been infected and for the corresponding antibody response, the relative contribution of increasing, cross-reactivity, and antigenic seniority to its neutralising ability. By combining the model with data from a study in southern China investigating antibody levels against nine different flu strains from 1968 to 2009, we revealed key components of the immune response to influenza virus infection, and received estimates of the frequency of influenza infection and the ages at which the infection occurred. Our results indicate that “antigen seniority ?, with strains found earlier in life getting more “senior ? positions in the immune response, and short-lived cross-reactivity between different strains are important components of the immune response and can therefore shape the development and onset of influenza virus. Citation: Kucharski AJ, Lessler J, Read JM, Zhu H, Jiang CQ, Guan Y, et al. (2015) Assessment of influenza life cycle A(H3N2) Antibody response from cross-sectional data. PLoS Biol 13.3: e1002082. https://doi.org/10.1371/journal.pbio.1002082 Academic editor: Andrew Fraser Read, The Pennsylvania State University, USA Received: 16 April 2014; Approved: 16 January 2015; Published: 3 March 2015 Copyright: © 2015 Kucharski et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which allows unlimited use, distribution and reproduction in all media, provided that the original author and source are credited Data Availability: Data on observed individual titres is available as Dataset S1 in DOI: 10.1371/journal.ppat.1002802. Model-estimated titres are available in S1 Data. Funding: This work is supported by the Medical Research Council (UK, Project MR/J008761/1); Wellcome Trust (UK, Project 093488/Z/10/Z); Fogarty International Centre (USA, R01 TW008246-01); Fogarty International Centre with the Science & Technology Directorate, Department of Homeland Security (USA, RAPIDD program); National Institute for General Medical Sciences (USA, MIDAS U01 GM110721-01); and the National Institute for Health Research (UK, for Health Protection Research Unit finance). JL's work was supported by a contribution from NIAID (K22 AI092150-01). The financiers had no role in study design, data collection and analysis, decisions to publish, or drafting the manuscript. Competing interests: The authors have explained that there are no competing interests. Abbreviations: HA, haemagglutinin; HAI, haemagglutination inhibition; NA, neuraminidase Introduction The immunity of a host population against specific influenza A strains may affect a number of important biological processes. It may affect the emergence of new strains of viruses and thus shape the development of the disease [1,2]. It may also affect the size and severity of a pandemic [3–6] and the effectiveness of vaccination programmes [7]. There are two main ways to measure the adaptive immune response to influenza virus [8]. In microneutralisation tests, a mixture of viruses and diluted serum is used to infect cell cultures; the titre is the highest dilution for which viral infection is blocked. Microneutralisation titres therefore measure the total neutralising antibody response. Such a response may contain several components. Some antibodies are specific to antigenic sites on the globular head of haemagglutinin (HA) surface protein. These sites are very diverse: HA undergoes frequent mutation, allowing the virus to escape existing antibody responses [9]. There is also evidence that the antibodies have preserved epitopes on the stem of the HA protein or neuraminidase (NA) surface protein [10-12]. Alternatively, the haemagglutination inhibition test (HAI) measures the extent to which antibodies inhibit the binding of the HA protein to red blood cells. Microneutralisation titres are likely to capture more of the total antibody response, but the HAI test is a more sensitive measure of antibodies specific to antigens on the head of the HA protein [13]. The ability of human serum to neutralize current and historical influenza strains shows significant variation between individuals and with age [10,13-15]. These patterns are likely to be influenced by a number of factors. First, neutralisation titres to a particular strain are due to the immune response after exposure to this virus: after infection or vaccination, the immune response to a particular virus may be increased [5]. Although the first response may fall below a short period of time [16.17], there is evidence that the subsequent level of reaction may persist for several decades [18]. Observed titres may also depend on the order and number of influenza infections. Francis [14] coined the term “original antigen sin ? to describe the phenomenon by which HAI titres to the first influenza infection in a lifetime were apparently higher than titres to other strains. In the case of subsequent infection, it has been suggested that the original response may be enhanced [14,19-21] and the antibody response to the new strain is reduced [22]–26], in order for original antigenic sin to occur there is evidence that the original and new strain must be antigenically related [26]. Recent work has refined the original antigenic sin hypothesis, suggesting that serological patterns should be described in terms of “antigen seniority ? [13.15]. As with original antigen sin, the primary infection gets the most “senior ? position in the immune response, but – as an important refinement to the original sin hypothesis – the hierarchy of responses continues with each subsequent infection, as each strain takes a less senior position in the response. In addition to increasing and original antigenic sin/antigen seniority, observed serological responses may also be due to cross-reactivity between strains and temporal slowdown of response. Although hosts have not been exposed to a particular strain, they may have an elevated titration against the virus if the test strain is similar to those already found [5.18]. However, determining the contribution of different mechanisms to neutralisation titres remains a challenge, as the overall effect of immune responses over a lifetime is rarely observed directly [13.24). In addition, observed titres not only depend on the relationship between infection and immune response: they are also affected by the specific strains a host has been infected with. It has been suggested that some age groups are infected more frequently than others [28,29], but the actual rate of influenza infection cannot be easily measured [30]. To investigate the effects of previous infections and subsequent immune responses to observed microneutralisation titres, we adapted a mechanistic model of serological dynamics within the host animal to data from a cross-sectional survey based in southern China [31]. The study tested 151 individuals' serum against a panel of nine different influenza A(H3N2) strains isolated between 1968 and 2008. Six of these strains corresponded to representative viruses from each second “antigenic clusters ? They occurred between 1968 and 2003; in total there were 11 such clusters of antigen-like strains during this period [32]. The other three test viruses were strains circulating in southern China between 2003 and 2008. We used the mechanistic model to assess the relative contribution of increasing, cross-reactivity, and antigen seniority to observed neutralization titres, and estimated important immunological parameters. We also estimated which specific strains each individual had been infected with, and thus collected infection history for each individual in the study population. This enabled the rate of infection of influenza A(H3N2) in the host population to be calculated, and to determine how the rate of infection varied with age. Materials and Methods Ethics Statement Study protocols and instruments were approved by the following institutional review boards: Johns Hopkins Bloomberg School of Public Health, University of Liverpool, University of Hong Kong, Guangzhou No. 12 Hospital, and Shantou University. Written informed consent was obtained from all participants over 12 years of age, and oral consent was obtained from participants aged 12 years or younger. Written permission from a legal authorised representative was obtained for all participants under the age of 18. Data Participants were recruited from five study places, with 20 households randomly selected at each location (additional data are given in Lessler et al. [31]). The participants serum was tested against nine representative strains of influenza A(H3N2) using a virus neutralisation analysis. The strains included six vaccine strains: A/Hong Kong/1/1968, A/Victoria/35/1975, A/Bangkok/1/1979, A/Beijing/353/1989, A/Wuhan/359/1995 and A/Fujian/411/2002. Three strains circulating in southern China in the years prior to the study were also tested: A/Shantou/90/2003, A/Shantou/806/2005 and A/Shantou/904/2008. Titrer was measured with serial 2-fold dilutions from 1:10 to 1:1,280 in duplicate. In our analysis, we represented the results in terms of log neutralization titres. A log titre of c corresponded to a dilution of 10 × 2 c−1). Therefore, there were nine possible log titres: the lowest was 0, corresponding to a dilution < 1:10; the highest was 8, corresponding to a dilution of 1:1,280. Model of serological dynamics We took an “epochal ? view of infection [32.33], with individuals either infected or not during each antigenic era; we assumed that there were 14 such eras between 1968 and 2008. We modeled serological titres by assuming that medium neutralization titres to a specific strain depended on both individual infection history and a combination of serological mechanisms. We considered four specific mechanisms: increase from infection with the test strain, cross-reactivity from antigen-like strains, increase in previous responses as a result of subsequent infection, and suppression of subsequent responses as a result of previous immunity. The last two mechanisms have been proposed as potential explanations for observed patterns of antigen seniority [13.15]. We included the four main mechanisms in the model as follows. Suppose an individual has an infection history that consists of a set of strains X (note that we do not distinguish between live infection and vaccination in the model). We assumed that if an individual had been infected with only one strain, they would exhibit a fixed log titre against that strain, controlled by a single parameter, μ. Therefore, in the absence of either seniority or cross-reactivity, the individual would have titration equal to μ for each strain in its infection history, and zero for all other strains (Fig. 1A). However, if the individual had been infected with more than one strain, titres against previous strains could be higher than those against later strains as a result of antigen seniority. PPT PowerPoint slide PowerPoint slide PNG larger image larger image TIFF original image Download: Figure 1. Schematic of mechanisms that shape observed titres in the model. (A) Easily increase. If an individual had been infected with a particular strain, in the absence of cross-reactivity and antigen seniority they showed a fixed response to this strain corresponding to μ. This was controlled by a single parameter in the model. In the figure, the strains are sorted by isolation date, with serological samples taken today. Stem animals with which the host has been infected are shown in red; coloured bars show the magnitude of observed log titres as a result of previous infection with each strain. (B) Increase in previous response via antigen seniority. Infections boosted observed titres to previously infected strains with a certain scale factor, controlled by parameter τ1. The size of titres to a particular strain therefore depends on the number of infections that occurred after infection with that strain. (C) prevention of new reactions via antigen seniority; Responses to each strain were reduced as a result of immunity generated by previous infections. This decrease was controlled by the parameter τ2. The titre to a particular strain therefore depended on the number of infections that occurred before this strain was circulated. (d) cross-reactivity; In the absence of antigen seniority, the observed titre to a test strain was dependent on response due to infection with this strain, plus cross-reactive responses from infection with other strains. These cross-reactive responses decreased with the distance (measured this year) between each infection and the test strain. Stem that circulated further from the test strain in time contributed less to the observed response. https://doi.org/10.1371/journal.pbio.1002082.g001 Two mechanisms have been proposed to explain observed patterns of antigen seniority: previous responses may be enhanced as a result of subsequent infections, or subsequent responses may be reduced as a result of previous immunity [15]. To evaluate the contribution of these two mechanisms, we specified the model so that – depending on parameter values – both, one, or none of the mechanisms could contribute to measured titres. Therefore, during the adaptation process, the model results could be compared with observed serological data to determine the most reasonable mechanisms for the data. First, we assumed that any infection could increase titres against strains previously encountered by a parameter τ 1 (Fig. 1B). Therefore, the titre μ was scaled by a factor s 1 (X, j) = (1 + τ 1 )<unk>X<unk>N j where N j is the number of the strain in the history of infection (i.e. the first strain is 1, the second is 2, etc.) and “”X” is the total number of infections. If τ 1 = 0, then there was no increase as a result of subsequent infection. This mechanism, in which observed titres to a certain strain depended on the number of subsequent infections, was also proposed by Miller et al. [13] following a longitudinal study of influenza A infections. Second, we assumed that earlier immunity could reduce observed titres against strains found later in life (Fig. 1C). The titre against a certain stem would therefore be scaled by a factor . Here, observed titres to each strain were due to how many infections had occurred in the past. When τ 2 = 0, previous infections did not reduce response to later strains. When τ 2 was large, the formulation corresponded to a model of original antigen sin, in which immunity from the primary infection suppressed all subsequent responses [28.34]. Finally, we incorporated cross-reactivity by assuming that mean titres against a specific strain were equal to the sum of cross-reactive responses to all strains in an individual's infection history. We assumed that the contribution of each strain depended on the time distance between the strain in infection history and the test strain (Fig. 1D). The cross-reaction level between a test strain j and the infecting strain m was given by d(j, m) = e′′t m −t j −′′, where <unk>t m −t j −′ was the number of years between strains j and m, and If . . . was large, it did not correspond to any cross-reactivity between the tribes. To combine the four mechanisms of the model, we assumed that the log titre individual i has against a strain j was Poisson distributed with the following mean values: 1) As d(j, m), p 1 (X, m), or p 2 (X, m) could correspond to 1 for certain parameter values, the model was capable of omitting certain mechanisms if necessary. We also explained a potential observational error by assuming that there was a uniform probability of observing a titration different from the true one. The probability of observing titer c j against test strain j was therefore equal to the sum of all possible true titres: (2) We estimated model parameters using the Markov chain Monte Carlo (details in S1 Text; data set and model outputs in S1 Data). As a sensitivity analysis, we also included decreasing antibody responses in our model. This was achieved by modifying equation 1: (3) where w was a decreasing parameter that we fixed. The wording meant that decreasing titres to strains in infection history were reduced by one factor e−w per year. If w = 0, then the model given by equation 1 was found. Discussion We have investigated how previous influenza A(H3N2) strains affect observed cross-sectional neutralization titres. We found that “antigen seniority? and rapidly decaying cross-reactivity were important components of the immune response. The order in which an individual is infected with influenza strains was therefore important in dictating observed titres to a particular virus: titres appeared to be the result of a combination of strain-specific increase, cross-reactivity and suppression of subsequent responses due to antigen seniority (Fig. 5. PPT PowerPoint slide PowerPoint slide PNG larger image larger image TIFF original image Download: Picture 5. Schematic of mechanisms that shape observed titres. Our model suggests that the expected size of titres resulting from a sequence of infections depends on three of the four mechanisms described in Fig. 1: simple increase, suppression of subsequent responses due to antigen seniority, and cross-reaction. The contribution of infecting strains to observed titres is affected by simple increase and suppression via antigen seniority (A). These contributions, as well as cross-reaction between similar strains, affect the final observed titres (B). For illustrative purposes, here tribes appear in 3-y-long eras, and have circulated over a 40-y period. Stem animals with which the host has been infected are shown in red; coloured bars show the magnitude of observed log titres as a result of previous infection with each strain. https://doi.org/10.1371/journal.pbio.1002082.g005 Our results emphasize the importance of understanding how currently unnoticed mechanisms shape the dynamics of influenza for individuals during their lifetime. Traditionally, analysis of serological data has been descriptive rather than mechanistic. It has therefore been difficult to distinguish between different hypotheses that could describe observed patterns. In particular, we evaluated two mechanisms of antigen seniority that have been proposed as explanations for why individuals exhibit elevated titres to strains found earlier in life [15]: previous responses may be enhanced by subsequent infections, or subsequent responses may be reduced as a result of previous immunity. There is a clear discrepancy between our main parameter estimates for antigen seniority and empirical observations of increases in antibodies against early infections. Our baseline results (in the absence of decline) suggest that although there is a decrease in the extent of response to subsequent infections, the reinforcing component of antigen seniority has a negligible effect on observed long-term titres. However, several studies have found evidence to increase existing responses after influenza infection [13,14,19-21]. There were two mechanisms in our model that could potentially lead to increased titres to previously encountered strains after subsequent infections. The first was cross reaction: within our framework, individuals who had been infected with few strains (Fig. 3A) had lower titres than those that had been infected with multiple strains that are antigenically related (Fig. 3C). We assumed cross-reaction was symmetrical in the model, and made the same contribution to titres regardless of the order of infection. On the other hand, we found no evidence that previous reactions had increased as a result of antigen seniority. To investigate this further, we considered the possibility of decreasing antibodies as a sensitivity analysis. Unfortunately, cross-sectional data did not identify the rise and slowdown processes. Therefore, we assumed a reasonable total antibody slowdown and found evidence to increase as part of an antigen seniority process. However, although it was possible to force reinforcement into the model, we propose that the issues of identity between the increase and decrease and the discrepancy between the model parameters and observed increase are both the consequences of the different timeframes on which these immunological processes arise. It is both likely that booster and decreasing contribute to the hierarchal nature of antibody responses to influenza. However, although the decline in elevated titres has been observed over periods of less than one year [35-38], it is not clear exactly how enhanced antibody responses persist over time in the absence of infection [41]. Based on our model results, we suggest that repeated increases in persistent antibody responses in the absence of decline are unlikely: such a process would lead to either extremely high titres in older individuals or very low rates of infection, none of them seem credible. Therefore, we believe that our model provides a reasonable description of the acquisition of a stable set of persistent antibodies. The apparent differences between observed boosters and relatively low titres to historical strains further underscore the need for studies that make repeated measurements of individuals' serological responses against a panel of historical influenza strains [42]. With such data, the mechanistic model presented here could be expanded to explore both the short-term and long-term dynamics of influenza immunity. This would help clarify the exact role of boosting and suppression in antigen seniority. We found that cross-reactivity quickly decayed over time, with a half-life of 2.4 y. Therefore, there was little cross-reaction between influenza A (H3N2) strains that circulated several years apart. We also considered a model that included broad cross-reactivity between strains, but when we adapted this model to data, the parameter estimate for broad cross-reactivity was zero, suggesting that this additional component was not necessary to reproduce observed serological patterns. However, there is evidence that individuals can produce largely cross-reactive antibodies after infection with a pandemic strain [12.40], and that individuals may exhibit a longitudinal increase in neutralising titres against pandemic strains no longer circulating [13]. This again highlights the need for longitudinal studies of serological responses against a panel of historical influenza strains. Such data would allow for joint examination of the contribution of broad and strain-specific immune responses, and understanding how cross-reactive antibodies and antigen seniority affect observed serological patterns across multiple time scales. In addition to comparing the effects of different immune mechanisms, we estimated the infection history of each individual in our study population. We used this information to measure how the infection rate varied with age. Although infections became less common as individuals progressed through childhood and young adulthood, they occurred at a similar rate for individuals over the age of 30 (Fig. 4A). It has been suggested that flu transmission is driven by intense social contacts among younger age groups [43]. The decrease in infection rates with age may be due to age-specific differences in social behaviour. A study conducted in the same area of southern China as our serological survey found intense mixing within the age groups under 20, which could mean that the infectious power was higher within these groups [44]. Unfortunately, we had limited serological data for very young individuals (the youngest participant in the study was 7 years old); it would be interesting to see how the infection rate changes from birth to childhood. There are a few further limitations to the work described here. We made no prior assumptions about the infection rate of different age groups, and thus infection history, in the model. An important next step would be to develop an approach that could measure the infectiveness of cross-sectional data [45]. This could be investigated using a model that was responsible for both population transfer dynamics and serological responses. In addition, we investigated serological data from only 151 participants in southern China. It would therefore be helpful to test similar models of serodynamics against observed titres in other populations [42]. We also focused on reactions to a panel of A(H3N2) flu strains. Unlike influenza viruses in Group 1 such as A(H1N1), A(H1N1p) and A(H2N2), no virus in Group 2 other than A(H3N2) has caused a pandemic; it has been suggested that this is the reason why antibody titres specific to HA stalks may be lower for Group 2 viruses than for the more antigenically diverse viruses in Group 1 circulating in humans [13]. We also do not distinguish between live infection and vaccination in the model; different routes of exposure may affect the sin/seniority of antigen process in different ways [12,24]. The model we present offers a new method for simultaneously examining immune responses and previous infections. Studies investigating the antigenic association between different flu strains typically investigate cross-reactivity with ferret serum [9.32]. However, the transmission dynamics of influenza are mediated not only by antigenic changes in the virus, but also by underlying immunity in the host population. In order to analyse the evolutionary pathway of influenza viruses using human serum, it would be necessary to take into account previous infections, and how these shape the immune response. We propose that a model of serodynamics, as described in this document, should provide the theoretical basis necessary to address this problem. Our results also have implications for the analysis of control measures. By considering how a lifetime of infection shapes cross-sectional serum, we have measured the relative importance of various immune mechanisms and previous infections in measured serological responses to influenza. In addition to influencing the development of influenza, such mechanisms may affect the effectiveness of vaccination programmes [7]. We found that the model had limited capacity to accurately predict the size of observed titres to strains circulating several years before or after the strains on which the model was mounted (S8 Fig.). This is likely the result of the rapid decay of cross-reactivity between strains over time. However, the model was generally able to predict age-specific trends in titres to non-contrahed strains, even those far from the strain used for assembly. It also reproduced the observed titration levels exactly when the strains were near the test strains in time. This suggests that sufficient representation of previous strains, perhaps from each antigenic era, would be needed for all responses to be reproduced accurately. In addition, our results were based on neutralization titres. Similar results are expected to be achieved with HAI, albeit with lower specificity for lower titre values [15.42]. Future studies may also benefit from emerging immunological techniques based on high throughput of protein microarrays [46] and sequence-based measures of B-cell diversity [47]. Using a model of cross-sectional serological responses, we have assessed the relative importance of various immune mechanisms and the timing of influenza infection to shape observed neutralization titres during an individual's lifetime. As far as we know, these two key factors have not previously been combined to fit immunological data. In addition to characterizing different aspects of the immune response, we have generated individual estimates of the frequency and age distribution of influenza infection from cross-sectional serological data. These results show the value of interpreting immune responses associated with a lifetime of infection. Therefore, integrating the life course of immunity into future analyses of influenza dynamics could lead to a better understanding of population sensitivity and the potential transferability of new seasonal strains. Author's contribution Perceived and designed the experiments: AJK SR. Performed the Experiments: AJK. Analyzed data: AJK SR. Contributed reagents/materials/analyses: JL JMR HZ CQJ YG DATC SR. Written the magazine: AJK JL JMR DATC SR. Designed the empirical study and collected data: JL JMR HZ CQJ YG DATC SR. <unk> <unk> What is a summary?
– Historiskt sett har det varit svårt att fastställa hur ofta människor får influensa, delvis för att de flesta av oss inte kan själv diagnostisera det, och tror att vi har det när vi inte vet eller inte vet att vi har det när vi har det. Nu säger forskare att de har hittat svaret – att barn får influensa i genomsnitt vartannat år, vuxna vart femte år – genom att studera antikroppar i blodproverna på 151 personer i södra Kina för att reda ut vilka influensastammar människor har blivit immuna mot och när. Genom att jämföra en persons antikroppar mot vissa stammar med de datum som dessa stammar cirkulerade, kunde forskarna fastställa när människor faktiskt hade influensa, rapporterar teamet i tidskriften PLoS Biology. "Den exakta infektionsfrekvensen varierar beroende på bakgrundsnivåer av influensa och vaccination", berättar en forskare för LiveScience. Forskarna, från Kina, USA och Storbritannien, säger att det finns flera möjliga orsaker till att barn får influensa mer än dubbelt så ofta som vuxna, bland annat att de utsätts för fler bakterier på grund av ökad interaktion med andra, och också att de helt enkelt inte har levt lika länge och inte har samlat på sig samma antikroppar mot influensastammar. Men det är intressant att lägga märke till att barn har starkare immuna reaktioner mot influensan än vuxna. Resultaten kan hjälpa forskare att bättre förutsäga hur stammar kommer att anpassa sig i framtiden, samt hur immunitet mot tidigare stammar kan påverka ens svar på nya vacciner. (Årets influensavaccin var ett av de minst effektiva under det senaste årtiondet.)
“Historically, it has been difficult to determine how often people get the flu, partly because most of us cannot diagnose it ourselves, and think we have it when we don’t know or don’t know we have it when we have it. Now researchers say that they have found the answer – that children get flu on average every two years, adults every five years – by studying antibodies in the blood tests of 151 people in southern China to find out which flu strains people have become immune to and when. By comparing a person’s antibodies against certain strains with the dates these strains circulated, scientists were able to determine when humans actually had the flu, reports the team in PLoS Biology magazine. "The exact infection rate varies depending on background levels of influenza and vaccination," a researcher tells LiveScience. The researchers, from China, the United States, and the United Kingdom, say that there are several possible reasons why children get flu more than twice as often as adults, including exposure to more bacteria due to increased interaction with others, and also that they simply have not lived as long and have not accumulated the same antibodies against flu strains. Interestingly, however, children have stronger immune reactions to the flu than adults. The results can help researchers better predict how strains will adapt in the future, as well as how immunity to previous strains can affect one's response to new vaccines. (The influenza vaccine of the year was one of the least effective in the last decade.)