A fundamental unsolved issue in vaccine design is how neutralizing antibodies and cytotoxic CD8+ T cells cooperate numerically in controlling virus infections. We hypothesize on a viewpoint for the multiplicative cooperativity between neutralizing antibodies and CD8+ T cells and propose how this might be exploited for improving vaccine-induced protective immunity.info:eu-repo/semantics/acceptedVersio

Argilaguet, Jordi

Bocharov, Gennady

Grebennikov, Dmitry

Meyerhans, Andreas

Trends in Immunology

English

IRTA Pubpro

            This document is a postprint version of an article published in Trends in Immunology© Elsevier after peer review. To access the final edited and published work see https://doi.org/10.1016/j.it.2021.08.003    Document downloaded from:          1  1  2 Examining the cooperativity mode of antibody and CD8+ T cell immune 3 responses for vaccinology  4  5  6  7 Gennady Bocharov1,2,3*, Dmitry Grebennikov1,2,3, Jordi Argilaguet4, Andreas Meyerhans5,6* 8 1Marchuk Institute of Numerical Mathematics, Russian Academy of Sciences, 119333 9 Moscow, Russia 10 2Moscow Center for Fundamental and Applied Mathematics at INM RAS, 119333 Moscow, 11 Russia 12 3Sechenov First Moscow State Medical University, 119991 Moscow, Russia 13 4IRTA, Centre de Recerca en Sanitat Animal (IRTA-CReSA), Campus de la Universitat Autònoma 14 de Barcelona, 08193, Bellaterra, Spain 15 5Infection Biology Laboratory, Department of Experimental and Health Sciences, Universitat 16 Pompeu Fabra, 08003 Barcelona, Spain 17 6ICREA, Pg. Lluís Companys 23, 08010 Barcelona, Spain 18 *corresponding authors 19   20  21 Abstract 22  23 A fundamental unsolved issue in vaccine design is how neutralizing antibodies and cytotoxic 24 CD8+ T cells cooperate numerically in controlling virus infections. We hypothesize on a 25 viewpoint for the multiplicative cooperativity between neutralizing antibodies and CD8+ T 26 cells and propose how this might be exploited for improving vaccine-induced protective 27 immunity.  28             29  30  31  32  33  34  35  36  2  37 The ongoing SARS-CoV-2 pandemic reminds us about the devastating potential of pathogenic 38 viruses for our well-being and the importance of protective vaccines to keep them under 39 control. Indeed, today, vaccines are among the most efficient and cost-effective weapons to 40 combat infectious diseases. Following immunization, upon reinfection, a pathogen 41 encounters an increased number of pathogen-specific antibodies and antigen-specific T 42 lymphocytes, and the race -- i.e. numbers game [1] -- between pathogen expansion and 43 immune-mediated pathogen elimination is shifted in favor of the host. As a consequence, the 44 vaccinated individual is, in the best-case scenario, either protected from infection or from 45 severe disease [2].  46  47 Due to significant progress in (i) virus structure determination by cryo-electron microscopy 48 [3], (ii) rapid and massive sequencing technologies that enable the timely characterization of 49 emerging viruses and the analysis of systems responses upon vaccination (systems 50 vaccinology)[4], (iii) the introduction of mRNA-based vaccines [5] and (iv) the understanding 51 of immune-regulatory mechanisms and network regulations [6,7], a transformation towards 52 rational vaccine design strategies has recently taken place [8,9]. However, in vaccinology, 53 many questions and major challenges remain to be robustly addressed and a more complete 54 understanding of the relationships and cooperativity between humoral and adaptive 55 immunity in response to vaccines is imperative. 56  57 With the aim to empower vaccines based on a more complete engagement of different 58 immune mechanisms, in this Forum article we suggest that vaccine protection thresholds may 59 be optimized by exploiting the cooperativity between humoral and adaptive immune 60 response components. We hypothesize that upon vaccine administration, neutralizing 61 antibodies and cytotoxic CD8+ T lymphocytes (CTL) might contribute to protective immunity 62 against viruses in a multiplicative way. Indeed, the role of CTLs in vaccine-induced protection 63 remains to be more fully characterized [6], and their quantitative relationship with antibody 64 responses for vaccine success remain unclear. Based on theoretical grounds, it seems likely 65 that they amplify/synergize their effects, and presumably, this might occur in a non-linear 66 way, although it might also be species- and/or vaccine- dependent. We posit that this 67 question may be relevant for vaccine development as it might help define certain threshold 68  3 requirements to achieve protective immunity. Here, we argue that it might be partly 69 answered using a theoretical model that aligns with recent experimental observations. 70 However, future and robust experimental and clinical trials are evidently needed to further 71 examine and validate this model-based prediction of multiplicative cooperativity and its 72 relevance for vaccine design.  73  74 Immune protection requirements against virus infections 75 A simple, well-accepted basic model of virus infection dynamics considers free virus particles 76 (V), uninfected but susceptible target cells (C), and virus-infected cells (Ci) (Figure 1A)[10]. The 77 dynamics of the overall infection process can be expressed in 3 nonlinear differential 78 equations describing the turnover of V, C and Ci with the respective production rates v, c, 79 ci and elimination rates v, c, and ci ( = alpha;  = delta; Figure 1A). Virus propagation i.e. 80 virus infection and expansion within an infected host occurs when the basic virus 81 reproduction ratio R0 > 1 while virus is contained if R0 < 1 [10,11]. Neutralizing antibodies 82 inhibit the infection of susceptible target cells C which would increase v, while CTLs kill 83 infected cells Ci which would increase ci [10]. As both parameters appear in R0 as a product, 84 neutralizing antibody and CTL responses reduce R0 values in a multiplicative way and 85 therefore, both adaptive immune responses synergize rather than simply sum up in virus 86 inhibition [12]. This type of synergistic relationship was initially revealed when both arms of 87 the immune response were first considered in a mathematical model of antiviral immune 88 responses against Influenza A virus infection [12]. Analysis of the stability condition for an 89 infection-free steady state [12], which is equivalent to R0 < 1, indicated the multiplicative 90 effect of CTLs and neutralizing antibodies for the elimination of a virus infection. Thus, instead 91 of increasing a single arm of immunity by N-times (i.e. via vaccination), the same protective 92 effect might be achieved by a parallel increase of both arms by √𝑵 -times. For example, 93 increasing an antibody titer by 100-times would be equivalent to a 10-times simultaneous rise 94 of antibodies and CTLs in this model. We argue that these considerations might provide a 95 rationale when aiming to overcome the quantitative limitations of single-arm immune 96 response-oriented vaccines. 97  98  4 How do these considerations relate to real-world virus infections? For illustration purposes, 99 by analyzing acute human infections with Influenza A virus (IAV) and Hepatitis B virus (HBV), 100 calibrated mathematical models had been generated that have estimated the different 101 growth and elimination parameters for both infections [12,13]. This has enabled the 102 quantification of the contribution of individual branches of the immune response for virus 103 control (Figure 1B). For IAV with an R0 value of around 32, the net elimination rates (Figure 104 1A) of virus (v) and infected cells (ci) should be increased such that their product is 32 times 105 larger than the respective products in a naïve host [12]. Likewise, for HBV with an R0 value of 106 around 4, the product of the corresponding elimination rates should be increased over 4-107 times. With the described proportions of the elimination terms (see Figure 1B and 108 supplementary table S1), one can now estimate the threshold requirements to reduce R0 109 below 1 for either an increase in single arm or combined arms of immunity [13]. A 36-times 110 reduction of R0 in the case of IVA would require increasing IAV-neutralizing antibody titers by 111 166-times. The same R0 reduction could be achieved by an 830-times increase of IAV-specific 112 CTL numbers, or the simultaneous increase of both arms by 25- and 120-times, respectively. 113 The equivalent calculation for a 4-times reduction of R0 for HBV gives required increases of 114 52-times for HBV-specific antibodies, 240-times for HBV-specific CTLs and a combined 18- and 115 80-times increase for both, respectively (summarized in figure 1C). 116   117 The theoretically-derived estimates of the immune threshold conditions for virus control (R0 118 < 1) can be corroborated by empirical virus infection data for which calibrated mathematical 119 models do not exist. In a recent study, data were provided for vaccinated rhesus macaques 120 (15 animals per immunization group) with either a neutralizing antibody-inducing or a 121 neutralizing antibody- plus CTL-inducing vaccine against simian–human immunodeficiency 122 virus (SHIV) infection [14]. The combination of neutralizing antibody and CTL induction 123 reduced the threshold requirements for neutralizing antibodies to confer protection. Animals 124 with a mean neutralization infectious dose 50 (ID50) titer value of 800 remained uninfected 125 upon SHIV challenge while animals with a mean neutralization ID50 titer value of 50 became 126 infected (see [14], extended data Fig. 8). A mere 2.5-times increase from 0.1% to 0.25% of 127 virus-specific CTLs protected the animals with the low antibody titer (see [14], extended data 128 Fig. 9a). Thus, a 16-times decrease of the neutralizing antibody titer might be compensated 129 by just a 2.5-times increase in the number of CTL to confer protection against infection. Taken 130  5 together, the induction of neutralizing antibody responses and CTL could reduce the required 131 protective titer of antibodies by more than an order of magnitude (Figure 1D).  132  133 The current approaches of vaccine design concentrate on the definition of immunogenic 134 epitopes, adjuvants, delivery routes and vaccine formulations. While all these are 135 fundamental elements of vaccines and their success, the multiplicative cooperativity of the 136 humoral and cellular arms of the adaptive immune response highlights another consideration 137 for vaccine design and predicting possible response outcomes, namely, that both immune 138 arms should be induced to exploit their synergy including their time-deferred mode of 139 cooperation [15]. Indeed, we posit that this concept might provide a basis for understanding 140 why attenuated vaccines can be so efficient whereas subunit vaccines that focus on inducing 141 neutralizing antibody responses might lack sufficient epitopes to achieve robust cellular 142 immunity and thus, might result in suboptimal protection. However, this concept remains 143 conjectural and the complexity of vaccine responses cannot be understated. Nevertheless, 144 we propose that exploiting the concept of multiplicative cooperativity might be useful when 145 aiming to reduce the protective immune threshold requirements of a successful vaccine. A 146 lower threshold might then perhaps also help in controlling a wider spectrum of virus variants 147 that appear during outbreaks and that might need more stringent immune responses. We 148 anticipate that further data-driven and hypotheses-oriented modeling studies might assist in 149 these endeavors and are clearly warranted. 150  151  152 Acknowledgments 153 The authors are supported by the Russian Science Foundation (RSF grant no. 18-11-00171), 154 the Russian Foundation of Basic Research (RFBR grant no. 20-04-60157), the Moscow Center 155 for Fundamental and Applied Mathematics at INM RAS (agreement with the Ministry of 156 Education and Science of the Russian Federation No. 075-15-2019-1624), the Spanish Ministry 157 of Science and Innovation grant no. PID2019-106323RB-I00 (AEI/MINEICO/FEDER, UE), and 158 “Unidad de Excelencia María de Maeztu”, funded by the AEI (CEX2018-000792-M). 159   160  161  6 References 162 1  Zinkernagel, R.M. et al. (1985) On the role of viruses in the evolution of immune 163 responses. Br. Med. Bull. 41, 92–97 164 2  Iwasaki, A. and Omer, S.B. (2020) Why and How Vaccines Work. Cell 183, 290–295 165 3  Yao, H. et al. (2020) Molecular Architecture of the SARS-CoV-2 Virus. Cell 183, 730–166 738.e13 167 4  Pulendran, B. et al. (2010) Systems vaccinology. Immunity 33, 516–529 168 5  Pardi, N. et al. mRNA vaccines — a new era in vaccinology. , Nature Reviews Drug 169 Discovery, 17. (2018) , 261–279 170 6  Pollard, A.J. and Bijker, E.M. (2021) A guide to vaccinology: from basic principles to new 171 developments. Nat. Rev. Immunol. 21, 83–100 172 7  Pulendran, B. et al. (2021) Emerging concepts in the science of vaccine adjuvants. Nat. 173 Rev. Drug Discov. DOI: 10.1038/s41573-021-00163-y 174 8  Mascola, J.R. and Fauci, A.S. Novel vaccine technologies for the 21st century. , Nature 175 Reviews Immunology, 20. (2020) , 87–88 176 9  Creech, C.B. et al. (2021) SARS-CoV-2 Vaccines. JAMA 325, 1318–1320 177 10  Nowak, M. and May, R.M. (2000) Virus Dynamics : Mathematical Principles of 178 Immunology and Virology: Mathematical Principles of Immunology and Virology, Oxford 179 University Press, UK. 180 11  Grossman, Z. et al. (1999) Ongoing HIV dissemination during HAART. Nat. Med. 5, 1099–181 1104 182 12  Bocharov, G.A. and Romanyukha, A.A. (1994) Mathematical model of antiviral immune 183 response. III. Influenza A virus infection. J. Theor. Biol. 167, 323–360 184 13  Marchuk, G.I. et al. (1991) Mathematical model of antiviral immune response. II. 185 Parameters identification for acute viral hepatitis B. J. Theor. Biol. 151, 41–69 186 14  Arunachalam, P.S. et al. (2020) T cell-inducing vaccine durably prevents mucosal SHIV 187 infection even with lower neutralizing antibody titers. Nat. Med. 26, 932–940 188 15  Oberhardt, V. et al. (2021) Rapid and stable mobilization of CD8+ T cells by SARS-CoV-2 189 mRNA vaccine. Nature DOI: 10.1038/s41586-021-03841-4 190  191  192  193  194  195  196  197  198  199  200  201  202  203  204  7 Figures 205  206  207 Figure 1.  Illustration of a mathematical model for fundamental immune interactions 208 providing protection against virus infections. (A) Basic model of virus infection dynamics at 209 the initial phase of infection. It describes the rate of changes (𝑑𝑑𝑡) of the population densities 210 of free virus particles (V), susceptible target cells (C) and virus-infected cells (Ci). The balance 211 of the growth and elimination processes determining their dynamics are described in the 212 right-hand-sides (r.h.s.) of the equations. The intensities of these processes are characterized 213 by the respective production rates v c, and ci and elimination rates v c and ci. The net 214 per capita elimination rate of the virus population is a sum of the natural degradation rate 0, 215 antibody-mediated elimination Ab[Ab], and the fraction of virus bound to target cells B 216 which then is unavailable for infecting other target cells. The net per capita elimination rate 217 of the infected cell population is a sum of the natural death rate and CTL-mediated 218  8 elimination, denoted by C0 and CTL[CTL], respectively. The levels of vaccine-induced 219 antibodies and CTL are denoted by [Ab] and [CTL], respectively. Green arrows indicate their 220 protective contribution to the elimination of an infection. The multifactorial parameter R0 221 characterizes the relative balance of infection spreading versus elimination. It can be defined 222 as the ratio between cell infection rate and infected cell elimination rate (r.h.s. of the third 223 equation in fig. 1A), estimated soon after an individual got infected. Because of this time 224 element in the definition of R0, the third term on the r.h.s. of the second equation is neglected 225 so that 𝐶 ≈𝛼𝐶𝛿𝐶 . Using the quasi-steady-state approximation for the viral load  𝑉 ≈𝛼𝑉𝛿𝑉𝐶𝑖, we 226 arrive at the displayed expression for R0. (B,C) Characteristics of IAV and HBV infections of 227 non-vaccinated human individuals according to calibrated mathematical models of both 228 infections [12,13]. (B) R0 values as well as relative values of virus and infected cell elimination 229 parameters. Numerical parameter values are in supplementary material Table S1. The units 230 of all parameters  are 1/day. (C) Hypothetical thresholds of antibody, CTL and antibody plus 231 CTL required for protection against IAV and HBV infection. Given are the times increase of 232 required amounts of virus-specific antibodies (Ab) and/or virus-specific CTL with respect to a 233 non-vaccinated naive human. (D) Hypothetical SHIV-specific neutralizing antibody 234 concentrations (in neutralization infectious dose 50 (ID50) titer values) and CTL numbers (in 235 percentage) that may protect or not protect against a SHIV challenge of rhesus macaques 236 based on [14]. This figure was created with BioRender.com. 237  238 Glossary 239  Production rate  -number of produced (target cells, infected cells, virions) over a given time 240 period. 241  Elimination rate  - number of eliminated (target cells, infected cells, virions) over a given 242 time period. 243  Virus reproduction ratio R0 – the relative balance of infection spreading versus elimination 244 within an infected host. If R0 > 1, a virus infection spreads, while it is eliminated if R0 < 1. 245 Stability condition for an infection-free steady state – a condition under which a virus 246 infection is cleared. This condition is mathematically expressed as an inequality relationship 247 on the rates of production and elimination which determines that the virus-free state is 248 stable. 249  9  250 Table S1.   251 Characteristics of IAV and HBV infections of non-vaccinated human individuals according to 252 calibrated mathematical models of the infections [12,13]. Given are the R0 values for both 253 infections as well as relative values of virus and infected cell elimination parameters. The units 254 of all parameters  and  are 1/day.  The intensities of the respective processes are 255 characterized by the respective production rates v, c, and ci and elimination rates v, c, 256 and ci. The net per capita elimination rate of the virus population is a sum of the natural 257 degradation rate 0, antibody-mediated elimination Ab[Ab], and the fraction of virus bound 258 to target cells B which then is unavailable for infecting other target cells. The net per capita 259 elimination rate of the infected cell population is a sum of the natural death rate and CTL-260 mediated elimination, denoted by C0 and CTL[CTL], respectively. The levels of vaccine-261 induced antibodies and CTL are denoted by [Ab] and [CTL], respectively. 262  263 Parameter Values for IAV Values for HBV nEliminatioGrowth0 =R  32.09 4.27 v · c · ci   (Growth)  174.4 0.0954 v · c · ci   (Elimination) 5.4043 0.0224 ci = ci0 + CTL · [CTL]    (Elimination of infected cells)  1.566 = 1.5 + 0.066  0.0527=0.052+0.00066 v = 0 + Ab · [Ab] + B   (Elimination of free virus) 3.45=1.7 + 0.731+1.02 0.4249= 0.4 + 0.0249+0.0000125  264  265 

Examining the cooperativity mode of antibody and CD8+ T cell immune responses for vaccinology

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Examining the cooperativity mode of antibody and CD8+ T cell immune responses for vaccinology

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