इन्फ्लुएंजा टीकाकरण संदर्भ का पहले से मौजूद प्रतिरक्षा

Although we develop influenza immunity from an early age, it is insufficient to prevent future infection with antigenically novel strains. One proposed way to generate long-term protective immunity against a broad range of influenza virus strains is to boost responses to the conserved epitopes on the hemagglutinin, the major surface glycoprotein of the influenza virus. Influenza-specific humoral immunity comprises a large fraction of the overall immune memory in humans, and it has been long recognized that preexisting immunity to influenza shapes the response to subsequent influenza infections and vaccinations. However, the mechanisms by which preexisting immunity modulates the response to influenza vaccination are still not completely understood. Using a set of mathematical models, we explore several hypotheses that may contribute to the diminished boosting of antibodies to conserved epitopes after repeated vaccinations.

सिस्टैंच ट्यूबुलोसा-सुधार प्रतिरक्षा प्रणाली

Except for severely immunocompromised individuals and highly isolated populations on remote islands, no one is immunologically naive to influenza. Even infants acquire immunity transferred to them from their mothers, which can affect the immune response to vaccination or infection (Albrecht et al. 1977; Halasa et al. 2008; Voysey et al. 2017; Willis et al. 2020). In unvaccinated populations, it is estimated that the first influenza infection occurs within the first 5 years of life (Bodewes et al. 2011). Influenza immunity is therefore established early in life and generates long-lasting immunity to the infecting strain. For instance, elderly individuals were largely protected from infection by the 2009 pandemic H1N1 strain (Centers for Disease and Prevention 2009), and it has been suggested that this is due to increased cross-reactive antibody titers elicited by antigenically similar H1N1 strains that circulated more than 60 years earlier (Hancock et al. 2009; Skountzou et al. 2010). Thus, immunity to influenza can last several decades. However, we can be re-infected with influenza multiple times as the virus constantly mutates and reassorts to escape preestablished immunity. It has been estimated that unvaccinated individuals are infected with a new strain of influenza every 3–7 yr (Kucharski et al. 2015).

There are two ways in which circulating influenza strains change over time. Antigenic drift arises from the gradual accumulation of mutations in the hemagglutinin (HA) and neuraminidase (NA) glycoproteinwhichat are the main antigens on the viral surface. Antigenically drifted viral strains can evade antibodies elicited following infection with previous strains, thus allowing the drifted virus to spread in the population. Antigenic drift is responsible for seasonal outbreaks of influenza and requires periodic reformulation of the vaccine. Antigenic shifts largely arise from the reassortment of zoonotic influenza strains. Antigenic shifts can cause more severe pandemics such as the 1957 and 1968 pandemics of H2N2 and H3N2 subtypes. Although there is significant sequence diversity between strains, there are some constraints on the variation of the HA protein. In particular, the mutations that cause antigenic drift are concentrated in the "head" of HA (the portion furthest from the membrane), and there are more conserved regions on the "stem" of the HA molecule that can elicit cross-protective antibodies (Doud and Bloom 2016; Neu et al. 2016). The effect of preestablished immunity on the immune response to influenza infection or vaccination has been a conundrum that has riddled the influenza field since its inception. Studies conducted in the 1950s demonstrated that there was an effect of preexisting immunity on subsequent immune responses to infection or vaccination (Francis 1955, 1960). To complicate matters, preestablished influenza immunity varies based on personal history of influenza exposure, which is highly variable, particularly between different age groups (Linderman et al. 2014; Gostic et al. 2019). Although the sequential exposure to antigenically distinct strains constantly shapes the antibody repertoire in a given individual, we might expect a boosting of responses to conserved epitopes with each new influenza infection. In animal model systems, an increase in the level of antibodies to the conserved region of the stem of HA has been shown to provide heterosubtypic protection (Wei et al. 2010; Kanekiyo et al. 2013; Krammer et al. 2013, 2014; Margine et al. 2013; Impagliazzo et al. 2015; Yassine et al. 2015). Similarly, there is evidence in humans suggesting that antibodies elicited by responses to virus subtypes in a given clade can provide limited protection to other viruses in the same clade (but not necessarily to viruses in a different clade) (Gostic et al. 2016). One longstanding question is why repeated influenza vaccinations and infections do not boost cross-reactive antibody titers to sufficient levels to prevent infection. Here, we review our understanding of human immune responses to influenza vaccination and how preexisting immunity affects the response to vaccination. We discuss the data on the magnitude of antibody responses to different epitopes of influenza and how these are boosted by vaccination. We then examine how simple mathematical models can be used to understand the factors contributing to the effect that preexisting immunity has on the boosting of humoral immune responses by vaccination.

सिस्टैंच ट्यूबुलोसा-सुधार प्रतिरक्षा प्रणाली

क्लिक करें यहाँ से देखें  Cistanche बढ़ाएँ प्रतिरक्षा उत्पाद

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उच्च पहले से मौजूद प्रतिरक्षा से इन्फ्लूएंजा में मनुष्य

Healthy adults accumulate high levels of immunity to influenza viruses via infestation vaccinations. For instance, inflreacha vaccine–reactive IgG, IgA, and IgM antibodies are readily measurable by enzyme-linked immunosorbent assay (ELISA) in the peripheral blood of healthy adults (Fig. 1vaccine–specificccine–specific memory B cells (MBCs) account for ∼1% of IgG MBCs in the blood. This is significantly higher than is found for other common vaccine antigens such as mumps, measles, rubella, or varicella-zoster virus ( VZV) (Fig. 1B, C; Rasheed et al. 2019; Eberhardt et al. 2020). Additionally, influenza vaccine–specific plasma cells account for ∼1% of total IgG plasma cells in the bone marrow (Fig. 1D; Davis et al. 2020; Eberhardt et al. 2020). This may be an underestimate for at least two reasons: there may be additional influenza-reactive MBCs and long-lived plasma cells to other influenza antigens not included in these analyses, and we are not detecting the responses to prior strains across-reactive crossreactive with the current vaccine strains.

क्योंकि का निरंतर वर्गीकरण पुनर्मूल्यांकन का परिसंचारी इन्फ्लूएंजा उपभेद, उपन्यास एपिटोप्स उठता ओवर समय, मुख्य रूप से इन द हेड ऑफ एचए%2सी और इन परिवर्तन अनुमति वायरस से पलायन इसके बावजूद पूर्व-अस्तित्व प्रतिरक्षा. इस प्रकार, to सुधार वैक्सीन प्रभावशीलता, हम आवश्यकता से बेहतर समझ कैसे पूर्वमौजूद प्रतिरक्षा प्रभाव क्षमता से बूस्ट क्रॉस-प्रतिक्रियाशील एपिटोप्स और उत्पन्न एंटीबॉडी से नया एपिटोप का वर्तमान में परिसंचारी वायरस।

मेमोरी रिकॉल रिस्पॉन्स आफ्टर वैक्सीनेशन

It has been previously described that there is a transient increase in plasmablast numbers in the peripheral blood at around day 7 postvaccination or postinfection (Wrammert et al. 2011; Li et al. 2012; Ellebedy et al. 2016). This cell population is largantigen–specification–specific and somatically mutated, suggesting that it is derived from a memory population that is rapidly activated upon reexposure to cross-reactive antigens. After primary vaccination or infection with a highly divergent influenza strain such as the 2009 pandemic H1N1 strain or a 2005 H5N1 strain, the majority of these plasmablasts are specific for the more highly conserved HA stem region (Wrammert et al. 2011; Li et al. 2012; Ellebedy et al. 2014). This is likely a result of the high frequency of crosstie-specific stem-specific MBCs and a low frequency of B cells to the head of the new influenza HA (because of the low sequence homology between HA head of novel pandemic and seasonal influenza strain-cursors precursor frequency of expanded MBC clones compared to naive B cells gives memory B cells an advantage during a recall response. Additionally, because of previous somatic hypermutation, MBC B-cell receptors are also expected to have higher antigenicity for antigen than naive B cells (Eisen et al. 1969). Selective recall of cross-reactive MBCs rather than exclusive activation of naive B cells likely contributes to the observation of "original antigenic sin" (OAS) (Francis 1955, 1960) in which the immune response is skewed toward cross-reactive epitopes that may have a lower affinity for the antigenically drifted strain. 

चित्रा 1. वयस्क उच्च स्तर का पहले से मौजूद प्रतिरक्षा से इन्फ्लूएंजा. इन्फ्लूएंजा वैक्सीन–प्रतिक्रियाशील IgG, IgA, और IgM एंटीबॉडी टाइटर्स थे मापा में स्वस्थ मानव वयस्क द्वारा एलिसा (n=38) (A). द आवृत्ति का प्रतिजन-विशिष्ट स्मृति बी कोशिकाओं विशिष्ट के के कण्ठमाला, खसरा, और इन्फ्लूएंजा (B), या VZV और इन्फ्लूएंजा (C) थे मापा द्वारा उत्तेजना का PBMCs और परिमाणीकरण द्वारा ELISpot as पहले वर्णित ( रशीद एट अल. 2019%3बी एबरहार्ड्ट एट अल. 2020). वीजेडवी और इन्फ्लूएंजा-विशिष्ट आईजीजी हड्डी मज्जा प्लाज्मा कोशिका थे परिमाणित द्वारा एलिस्पॉट और प्लॉट किए गए जैसे द प्रतिशत का कुल आईजीजी-स्रावित करने वाला हड्डी मज्जा प्लाज्मा कोशिका (डी)।

पहले से मौजूद एंटीबॉडी टाइटर्स AFFECT द मैग्नीट्यूड OF प्रतिक्रिया

Despite the advantages of memory recall after immunization, there is also evidence for interference of the immune response by preexisting memory. Typically, individuals with high preexisting cross-reactive antibody titers have a significantly reduced peak in circulating antigen-specific plasmablast numbers after influenza vaccination (Ellebedy et al. 2020). Individuals with higher preexisting antibody titers also have a reduced boost in antibody titers after vaccination (Ellebedy et al. 2014, 2020). Therefore, individual immune responses may be modulated by an individual's preexisting antibody titer, and as antibody titers to influenza wane over time, the extent of modulation may change over time. This became evident from immune responses to influenza vaccination in individuals who are vaccinated annually compared to individuals who have skipped influenza vaccination for at least 3 years. Figure 2 shows the response to seasonal influenza vaccination in these two groups of people. Figure 2A shows the relationship between the pre- and postvaccination antibody titers. Before vaccination, the antibody titers were significantly lower in individuals who skipped getting an influenza vaccination for at least 3 years (open squares) compared to individuals who received an influenza vaccination the previous year (filled squares). If antibody titers remain unchanged after vaccination, the data points would fall on the line with slope=1 (dashed line). An increase in antibody titers after vaccination would position the points above this line. If preexisting antibody titers do not affect boosting, we would expect the points to line up parallel to the dashed line. However, the best fit (solid line) has a slope less than one (least squares; slope=0.45; 95% CI=[0.37;0.68]), showing that individuals with lower preexisting antibody titers have significantly higher boost, as can be seen from the increased distance above the dashed line. Figure 2B shows the corresponding relationship between the prevaccination antibody titer and the magnitude of the boost, defined as a fold increase in antibody titer, for responses in the "skipped" (open squares) and "annual" (filled squares) groups. The fold increase (i.e., fold boost) of the antibody response reduces with an increase in the corresponding prevaccination antibody titer. Figure 2C shows how MBC frequency and preexisting antibodies may modulate these responses. With low levels of preexisting immunity, the reactivation of cross-reactive memory B cells becomes a dominant factor defining the response, whereas, with high levels of preexisting immunity, the negative feedback from the cross-reactive antibody may contribute to reducing the overall response.

सिस्टैंच ट्यूबुलोसा-सुधार प्रतिरक्षा प्रणाली

बूस्टिंग OF प्रतिक्रियाएं टू डिफरेंट एपिटोप्स

The immune responses to different epitopes following vaccination may be affected by the balance of preexisting memory B cells and preexisting antibody titers to these epitopes. For instance, after a primary immunization with a novel H5N1 strain, the specificity of antibodies elicited by vaccination is predominantly skewed to the more broadly cross-reactive stem-specific epitopes. This is a result of the higher number of preexisting stem-specific MBCs elicited by prior exposure to H1N1 that share cross-reactive stem epitopes, and the lack of preexisting memory to the novel H5 head domain. However, after secondary exposure to the H5N1 strain by booster vaccination, the immune response is highly skewed toward head-specific antibody responses, whereas stem-specific antibody titers remain largely unchanged. One potential mechanism for the dominance of HA head- versus stem-specific antibodies after secondary H5N1 vaccination is that high stem-specific antibody titers interfere with the memory recall response. Figure 3 plots the antibody response to head and stem after immunization with seasonal trivalent influenza vaccine (TIV) (Fig. 3A, D), high dose of novel H5N1 strain (Fig. 3B, E), and low dose of H5N1 strain with added adjuvant (Fig. 3C, F). Similar to the data for skipped versus annual vaccinees (Fig. 2), the fold boost of the antibody response to the HA head or stem is negatively correlated with prevaccination antibody titers to these epitopes. In the case of vaccination with seasonal TIV (Fig. 3A), the prevaccination titers of antibodies to both the HA stem and the HA head of H1N1 overlap. In this case, in which preexisting HA stem and head reactive antibody titers are similar, the fold boost in antibody titers is similar regardless of epitope specificity and correlates with the prevaccination titers (Fig. 3D). In the case of secondary vaccination with the novel H5N1 strain (Fig. 3B, C), there were higher levels of antibodies to the stem than the head of HA (and no individuals with very low titers to the stem). In this case, the boost in antibody titers was skewed toward the HA head domain, which exhibited significantly lower prevaccination titers.

चित्रा 2. बूस्टिंग एंटीबॉडी बाद इन्फ्लूएंजा टीकाकरण। इन्फ्लुएंजा वैक्सीन एंटीजन बाइंडिंग एंटीबॉडी टाइटर्स थे मापा गया द्वारा एलिसा पहले और 14 डी पोस्टवैक्सीनेशन इन व्यक्तियों जो था टीकाकरण the पिछला वर्ष (वार्षिक), और उन जो छोड़ा इन्फ्लूएंजा टीकाकरण के लिए पर कम से कम 3 वर्ष (छोड़ दिया गया). (ए) द रिश्ते बीच पूर्व- और पोस्टवैक्सीनीकरण एंटीबॉडी टाइटर्स. (बी) द रिश्ते बीच गुना वृद्धि में एंटीबॉडी टाइटर्स और प्रीवैक्सीनेशन टाइटर्स. (सी) ए योजनाबद्ध आउटलाइनिंग कैसे पूर्वमौजूद एमबीसी और एंटीबॉडी टाइटर्स मई प्रभावित द रिकॉल रिस्पांस आफ्टर सीजनल इन्फ्लूएंजा टीकाकरण।

चित्रा 3. एंटीबॉडी प्रतिक्रियाएं सिर और स्टेम ऑफ हेमाग्लूटिनिन (एचए). बूस्टिंग ऑफ एचए सिर- और स्टेम-विशिष्ट एंटीबॉडी निम्नलिखित टीकाकरण साथ निष्क्रिय एच1एन1 (से मौसमी त्रिसंयोजा इन्फ्लूएंजा वैक्सीन %5बीटीआईवी%5डी%2सी %5बीए%2सी डी%5डी%2सी उच्च-खुराक एच5एन1 %5बी बी%2सी ई%5डी%2सी और लो-डोज एच5एन1 साथ एडजुवेंट %5बीसी%2सी एफ%5डी). फॉर एच5एन1 हम उपयोग द डेटा से द बूस्ट टीका (देखें भी अंजीर. 6). (ए%ई2��सी) प्लॉट ऑफ पोस्ट- बनाम प्रीवैक्सीनेशन एंटीबॉडी टाइटर्स. अगर एंटीबॉडी टाइटर्स रिमाइंड अपरिवर्तित बाद टीकाकरण%2सी द डेटा पॉइंट्स विल फॉल ऑन द लाइन साथ ढलान %7बी%7बी15%7डी%7डी (धराशायी रेखा). द सर्वश्रेष्ठ-फिट लाइन (ठोस रेखा%2सी ढलान <%3बी 1) शो वह वहाँ अधिक बूस्टिंग जब द आद्याक्षर टाइटर्स हैं कम. (डी%ई2��एफ) पूर्व-टीकाकरण प्रतिरक्षा कम करता है बूस्ट और द रिश्ते है लगभग रैखिक पर ए लॉग-लॉग प्लॉट. (पैनल बनाया साथ डेटा से Ellebedy et al. 2014, 2020.)

मोडलिंग OF प्रतिक्रियाहरू TO THE HEAD AND STEM OF HA

Mathematical models of the humoral immune response can be used to explore different hypotheses for how preexisting memory B cells and antibodies affect the boosting of responses to different epitopes following immunization. Figure 4 shows a previously described simple two-epitope model that focuses on epitopes of the head and stem of HA and their interactions with B cells and antibodies specific to these epitopes (Zarnitsyna et al. 2016). Figure 4A shows a schematic for a basic model of humoral immune response ("basic" model) and its three modifications reflecting three additional mechanisms (color-coded). In the basic model, antigen-specific B cells are stimulated by the antigen, undergo clonal expansion, and produce antibodies. Stimulation of specific B cells continues until all the antigen has decayed. In the basic model, the fold increase in antibody titers is independent of prevaccination titers. There are several ways in which preexisting antibodies could reduce recall responses (Zarnitsyna et al. 2016). The antigen clearance model (ACM) hypothesizes that preexisting antibodies cause faster clearance of the antigen reducing the time for B-cell interaction with it and, thus, leading to less B-cell expansion and less generated antibody. The Fc receptor–mediated inhibition model (FIM) hypothesizes that preexisting antibodies form inhibitory immune complexes that reduce the activation of antigen-specific B cells by cross-linking B-cell receptor bound to the antigenic epitope and B-cell Fc RIIB bound to the Fc portion of the antibody in the immune complex (Nimmerjahn and Ravetch 2010). The epitope masking model (EMM) hypothesizes that preexisting antibodies and B cells specific for the same epitope would compete for binding to that epitope on the antigen, and it inhibits the proliferation of epitope-specific B cells. This happens as a result of physical constraints associated with the size of antibodies and does not affect the binding and proliferation of B cells specific for other epitopes that are spatially distant. In contrast to the ACM model, in which antigen is removed more quickly by binding to the antibody, in the EMM model, the production of antibodies is down-regulated despite the continued presence of antigen. When considering a simple two-epitope model with responses to the head and stem of HA (Fig. 4B), the antigen could be in four states, and the states are defined by antibody binding to the head and/or stem. The free antigen is named HS, whereas OS, HO, and OO represent antigens with antibodies bound to H (head epitope), S (stem epitope), or both epitopes, respectively.

चित्रा 4. योजनाबद्ध के लिए गणितीय मॉडल करने के लिए व्याख्या कम बूस्टिंग का एंटीबॉडी प्रतिक्रियाएं. (ए) योजनाबद्ध के लिए मूल एक एपिटोप मॉडल और इसके तीन संशोधनों शामिल तीन अलग तंत्र %3 ए बढ़ाया एंटीबॉडी बाध्य एंटीजन निकासी (एसीएम%2सी इन हरा)%2सी एफसी आर-मध्यस्थता निषेध (एफआईएम %2 सी इन नीला)%2 सी और एपिटोप मास्किंग (ईएमएम%2सी इन नारंगी)। (बी) द एक-एपिटोप मॉडल है विस्तारित से ए दो-एपिटोप मॉडल द्वारा सहित एक एपिटोप ऑन द हेड ऑफ एचए (एच) और एक एपिटोप ऑन स्टेम ऑफ एचए (एस). द बाइंडिंग ऑफ हेड-स्पेसिफिक एंटीबॉडी (एएच) और स्टेम-स्पेसिफिक एंटीबॉडी (एएस) डिफाइन्स फोर संभव स्टेट्स ऑफ द एंटीजन%3ए फ्री एंटीजन साथ नो एंटीबॉडी बाउंड (एचएस)%2सी एंटीजन बाउंड बाय एंटीबॉडी टू द हेड (ओएस)%2सी एंटीजन बाउंड बाय एंटीबॉडी टू द स्टेम (एचओ)%2सी और एंटीजन बाध्य द्वारा दोनों एंटीबॉडी (ओओ)।

Figure 5. Model discrimination. (A) Model predictions for the basic model and its modifications incorporating three different mechanisms: antigen clearance (ACM), Fc-receptor mediated inhibition of B-cell activation (FIM), and epitope masking (EMM). Using a two-epitope model, we predict how different levels of prevaccination head- and stem-specific antibodies affect the boosting of antibody titers to the head and stem of HA. Ten different sets of various prevaccination head- and stem-antibody titers (imitating 10 different individuals) were used for the prediction of the postvaccination fold boost in antibody titers. Responses to the head (red circle) and stem (blue triangle) in each individual are connected by a line. (B) The corresponding plots from three experimental studies. (C) Schematic illustrating how prevaccination memory B cells and antibodies affect the responses to prime and boost vaccinations with novel pandemic H5N1. High prevaccination frequency of stem-specific memory B cells defines the primary response with a strong recall of stem-specific antibodies. However, increased titers of stem-specific antibodies after the first H5N1 vaccination suppress further boosting of stem-specific antibodies following the secondary vaccination. Instead, the newly generated antibodies after the second vaccination are predominantly head-specific.

The three mechanisms incorporated in the ACM, FIM, and EMM models could each explain the reduction in the antibody fold boost due to preexisting antibodies, but we found that the two-epitope models make different predictions if we compare how responses to the head and stem of HA for each individual depend on prevaccination titers to head and stem (Fig. 5A; Zarnitsyna et al. 2016). For each individual, we assume different initial levels of antibody to the head (red circles) and to the stem (blue triangles), and we connect the predicted responses to the head and stem in a given individual after vaccination with a line. The boosting results for 10 different initial conditions mixing high and low initial antibody titers for the head and stem are shown. The basic model predicts that boosting will be independent of the level of preexisting immunity and, therefore, all lines are horizontal and at the same height. The ACM model predicts that antigen clearance equally affects the responses to the head and stem as binding of antibodies to any epitope will remove the entire antigen. Thus, the lines are parallel, and their heights are determined by the total preexisting antibody titers in each individual. In the FIM model, binding of antibody–antigen complexes (through the Fc portion of the antibody) to Fc RIIB on a B cell leads to inhibition of B-cell activation regardless of its epitope specificity. The FIM model predicts that preexisting antibody to either epitope similarly affects the generation of the responses to both the head and stem of HA and, thus, the lines for each individual are horizontal. The EMM model proposes that preexisting immunity only affects the response to the antibody-bound epitope, and the head- and stem-specific responses can be boosted by different amounts depending on their prevaccination antibody titers. More preexisting antibodies to a given epitope would result in more masking and less boosting of the response to that epitope, and the EMM model predicts that the lines connecting responses to head and stem for all individuals fall along a diagonal with a negative slope. We now compare the models' predictions with data from three vaccine studies in which individuals were vaccinated with HAs from H1N1 and H5N1 (data in Fig. 3). The experimental data were reanalyzed to generate a plot similar to that in Figure 5A by connecting antibody responses to the head and stem of HA with a line for each individual. For all three vaccination studies, the lines connecting responses to the head and stem in each individual (thin lines) and an average line using all the data (thick line) are shown in Figure 5B. We find that the average of the slopes from the thin individual's lines (not shown) is significantly different from zero. This suggests that responses to head and stem are boosted to different degrees in each individual and, thus, it is inconsistent with the ACM and FIM models in which inhibition of the head- and stem-specific responses is predicted to be similar. On the contrary, the averaged slope is not significantly different from the slope of fit to all data (thick line) supporting the EMM model.

सिस्टैंच ट्यूबुलोसा-सुधार प्रतिरक्षा प्रणाली

The EMM model assumes that responses to the head and stem of HA are generated independently, and preexisting titers to the head would not affect the boosting of antibodies to the stem (and vice versa). We would like to note that in all three considered vaccination studies the initial level of antibody to the novel head was relatively low. In this case, we do not expect much interference between the antibodies and B cells binding to the epitopes on the head with boosting of the responses to the epitopes on the stem of HA, and we found no significant correlation between the initial titer of head-specific antibody and fold boost in stem-specific antibody in these studies. However, this assumption may not be true in the case of high preexisting head-specific antibodies, and contributions of the different mechanisms described by the ACM, FIM, and EMM models on recall responses in different scenarios, for example, after repeated seasonal vaccination or infection, has yet to be determined.

एंटीबॉडी प्रतिक्रियाएं TO STEM OF HA दौरान प्राइम और बूस्ट H5N1 टीकाकरण

एक का शेष प्रश्न है क्यों द प्रतिक्रिया प्रति से स्टेम का हा था बूस्ट दौरान प्राथमिक टीकाकरण लेकिन नहीं माध्यमिक टीकाकरण साथ एच5एन1 (एलेबेडी एट अल. 2014%2सी 2020). हम उपयोग द सहायक H5N1 वैक्सीन अध्ययन जैसा एक उदाहरण जैसा यह उत्पन्न एक मजबूत समग्र प्रतिक्रिया. चित्र 6 दिखाता है फोल्ड बूस्ट ऑफ एंटीबॉडी से स्टेम का हा बनाम पूर्व-टीकाकरण टाइटर्स के लिए दोनों प्राइम और बूस्ट टीकाकरण साथ सहायक एच5एन1. प्रारंभिक एंटीबॉडी टाइटर्स से द स्टेम पहले प्राथमिक टीकाकरण थे कम तुलना से आद्याक्षर टाइटर्स पहले द माध्यमिक टीकाकरण%2सी और यह संभावित समझाता है अपेक्षाकृत बड़े गुना बूस्ट दौरान प्राथमिक टीकाकरण और सीमित बूस्ट दौरान माध्यमिक टीकाकरण। यह है समान से प्रतिक्रिया का वार्षिक और छोड़े गए टीके दिखाया गया में चित्र 2 में कौन व्यक्ति जो जो व्यक्ति जो था था अधिक हाल ही में टीकाकरण और उच्च पूर्व-मौजूद एंटीबॉडी टाइटर्स था कम गुना बूस्ट इन एंटीबॉडी टाइटर्स।

विचार-विमर्श

Influenza is a pathogen that exhibits extensive strain variation. This enables it to infect individuals multiple times, each time shaping and modulating influenza-specific immune memory. As a result, the level of immunity to influenza in humans is strikingly high, with influenza-specific MBCs commonly comprising at least 1% of total circulating MBCs. This is significantly higher than the frequency of MBCs specific for other commonly used childhood vaccine antigens (Fig. 1). In spite of the high level of acquired immunity, immune responses to the conserved epitopes on the virus are insufficient to prevent new infections. Here we analyze three vaccine studies in which immunity to influenza has been boosted with novel pandemic strains with the goal of understanding how preexisting immunity modulates the antibody responses to the conserved epitopes on the stem of HA. Preexisting immunity includes both memory B cells and antibodies, and although the increased frequency of memory B cells may benefit the secondary response, negative feedback from antibodies could limit it. We find that individuals with high preexisting cross-reactive antibody titers have a lower-magnitude response to vaccination with a reduced recall response despite high levels of preestablished MBC frequency (Figs. 1 and 2). Using mathematical models we analyzed the contribution of different factors such as MBC frequency, prevaccination antibody titers, increased clearance of antigen bound to the antibody in comparison to free antigen, inhibition of B-cell activation through binding of antibody–antigen immune complexes to inhibitory receptors on B cells, and epitope masking on the antigen by antibody to recall responses after vaccination (see models schematic in Fig. 4).

चित्रा 6. बूस्टिंग ऑफ एंटीबॉडी प्रतिक्रिया टू स्टेम ऑफ एचए के दौरान प्राइम टीकाकरण साथ सहायक एच 5 एन 1 वैक्सीन और कमी का बूस्टिंग बाद में बूस्ट टीकाकरण. भरा हुआ त्रिकोण और खुला त्रिकोण हैं द प्रतिक्रियाएं से स्टेम का हा बाद प्राइम और बूस्ट टीका%2सी क्रमशः. पूर्व-टीकाकरण प्रतिरक्षा कम करता है बूस्ट%2सी और द रिश्ते है लगभग रैखिक ऑन ए लॉग-लॉग प्लॉट. (चित्रा बनाया गया से डेटा इन एलेबेडी एट अल. 2020।

In the case of primary responses to novel influenza strains such as the 2009 pandemic H1N1 strain, or a highly divergent H5N1 strain, there are low preexisting antibody titers and higher preestablished cross-reactive HA stem–specific MBC frequencies in comparison to HA head–specific MBCs. Thus, in response to primary infection or vaccination with these novel strains, there is a strong recall response of crossreactive HA stem–specific antibodies. Intriguingly, upon secondary exposure to the H5N1 strain, the antibody response is predominantly skewed toward the HA head epitopes rather than the HA stem epitopes (Figs. 3 and 5). We hypothesize that this is due to the high preexisting HA stem–reactive antibody titers at the time of secondary vaccination (Fig. 5C) and explore three different mechanisms that may lead to the reduction of the response using simple mathematical models (Figs. 4 and 5). We found that the epitope masking model most closely recapitulates the data in the scenario of a response to a novel pandemic strain. We note that other considered mechanisms, such as antibody-enhanced antigen clearance and inhibition of B-cell activation by triggering inhibitory receptors on B cells by antigen–antibody complexes, may largely define the recall responses in different scenarios of vaccination and infection. The fact that the antibody response is skewed toward specificities not as prevalent in the antibody repertoire at the time of secondary H5N1 vaccination may be a beneficial outcome of epitope masking as it expands the immune repertoire.

We used a simple two-epitope model with one epitope on the head and one epitope on the stem of HA to make predictions for model discrimination. It has been established that HA has multiple epitopes on the head and fewer epitopes on the stem (Caton et al. 1982; Das et al. 2013). The incorporation of multiple epitopes on the head of HA into the model would require consideration of steric interference. Steric interference arises from the comparable size of the HA and antibody molecules and the proximity of head epitopes. Steric interference has been previously proposed in the context of neutralization efficiency of epitopes (Ndifon et al. 2009), and it is supported by stoichiometric studies of the binding of antibodies to the head of HA on the intact virus (Taylor et al. 1987; Taylor and Dimmock 1994). We can introduce a parameter that defines steric interference so that an antibody bound to a head epitope not only blocks the binding of further antibody or Bcell receptors to the same epitope but also inhibits binding to nearby epitopes on the head of HA. We have shown that multiepitope models with additional epitopes on the head of HA, and which consider steric interference between the epitopes on the head, generate dynamics and predictions similar to the two-epitope models shown in Fig. 5 (Zarnitsyna et al. 2016). In the studies described here, the preexisting head-specific antibodies were low at the time of vaccination as both H1N1 and H5N1 were novel pandemic strains. As there is no correlation between the preexisting antibody titers to the stem and the fold boost of antibody to the head, this suggests that stem-specific antibody titers do not affect the generation of responses to the head of HA (Zarnitsyna et al. 2016). In the case of seasonal drifted strains, we would expect higher levels of preexisting immunity to the head of HA, and head-specific antibodies may interfere with B-cell binding to the stem because of steric hindrance. Our modeling approach can be easily extended to consider asymmetric steric interference in which head antibodies could suppress the generation of stem antibodies but not vice versa. As the relevant experimental data becomes available, this should allow us to assess the potential role of asymmetric steric interference in boosting stem-reactive antibody titers during different scenarios of immunization or infection with antigenically drifted strains.

सिस्टैंच ट्यूबुलोसा-सुधार प्रतिरक्षा प्रणाली

Although the boost in antibody titers in response to vaccination was very low in subjects receiving the seasonal influenza vaccine who had been repeatedly vaccinated including the year before the study (Fig. 2), annual vaccination provides significant benefits. For instance, it is important not to overlook the fact that despite the increased magnitude of the response in skipped vaccinees, the postvaccination titers did not surpass that of vaccinees who had been vaccinated in the previous year. Additionally, although the memory recall response seems to be limited in repeat vaccinees with high preexisting cross-reactive antibody titers, this does not preclude the possibility that novel strain-specific antibody specificities are elicited by vaccination. As the magnitude of novel antibody specificities derived from naive B cells would likely be of significantly lower titers compared with the high preexisting cross-reactive antibody titers, this may not dramatically increase the overall titers but may still be immunologically relevant in the case of a viral challenge. Although there is a strong negative correlation between preexisting antibody titers and the magnitude of the boost of cross-reactive antibody titers, there may be an expansion of novel antibody specificities in germinal centers. Thus, repeated vaccination may affect and update the overall immune repertoire. The limited boosting of HA stem–reactive antibodies after secondary H5 vaccination and of antibody titers in repeated vaccinees raises questions regarding the limits of boosting cross-reactive antibodies. The epitope masking model predicts that increasing the antigen dose may overcome the limited boosting of antibodies because of the masking of epitopes on the antigen by preexisting antibodies (Zarnitsyna et al. 2016). Additional strategies such as the inclusion of adjuvants, novel vaccine platforms, or novel immunogens will likely be required for further boosting of broadly cross-reactive antibody titers.

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